> ## 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.

# Cortex Embed

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={["Snowflake"]} unsupportedWarehouses={["Databricks", "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 **Cortex Embed** transformation component lets you convert English-language text into a Cortex vector embedding. The component takes text from a single input column and uses it to create a vector embedding of either 768 or 1024 dimensions. The output of this component is a column containing the vector embedding string. This column will be named `embedding_result_<input-column-name>`.

To learn more about Cortex vector embeddings, read [Vector Embeddings](https://docs.snowflake.com/en/user-guide/snowflake-cortex/vector-embeddings).

To use this component, you must use a Snowflake role that has been granted the [SNOWFLAKE.CORTEX\_USER database role](https://docs.snowflake.com/en/sql-reference/snowflake-db-roles#label-snowflake-db-roles-cortex-schema). Read [Required Privileges](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions#label-cortex-llm-privileges) to learn more about granting this privilege.

To learn more about Snowflake Cortex, such as availability, usage quotas, managing costs, and more, read [Large Language Model (LLM) Functions (Snowflake Cortex)](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions).

### Use case

This component enables you to create embeddings directly in your transformation pipeline, which can then be used for semantic searching, clustering, and identifying similar content. For example, you can use this component to:

* Generate embeddings for video transcripts, which you can then search to find relevant content.
* Convert product descriptions into "Similar products" embeddings, which you can use to suggest products to your customers.
* Categorize support tickets by matching them to the appropriate support team.

***

## Properties

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

{/* <!-- param-start:[columnToEmbed] | warehouses: [snowflake] --> */}

<ResponseField name="Column to Embed" type="drop-down" required>
  Select the input column that will be used to create embeddings.
</ResponseField>

{/* <!-- param-start:[model] | warehouses: [snowflake] --> */}

<ResponseField name="Model" type="drop-down" required>
  The vector embedding model to be used to generate the embedding.

  For a vector embedding of 768 dimensions, choose one of:

  * snowflake-arctic-embed-m
  * e5-base-v2

  For a vector embedding of 1024 dimensions, choose:

  * nv-embed-qa-4
</ResponseField>

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

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