> ## 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.

# Score

Production use of this feature is only available on specific Maia plans. [Contact our Sales team](https://www.maia.ai/contact-us) for more information.

\<table><tbody><tr><td>Project Availability</td>
            \<td /></tr>
          \<tr><td>Component Type</td>
            \<td>Transformation</td></tr>
          \<tr><td>Connection Inputs</td>
            \<td>One</td></tr>
          \<tr><td>Connection Outputs</td>
            \<td>Unlimited</td></tr></tbody></table>

The **Score** transformation component uses the Google BigQuery [AI.SCORE](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-score) function and a Google Gemini model to rate each row of your input data against scoring criteria that you write.The component sends your scoring criteria and the values of the input columns you select to the model, and adds a column to the output that contains each row's score as a number.### Use caseThis component lets you rate and rank rows of data in your transformation pipeline. For example, you can use it to:* Rate customer reviews or survey comments on a scale that you define—for example, by how positive they are.
* Rank rows by score and keep only the highest-scoring rows for the rest of your pipeline.
* Prioritize support tickets or emails—for example, by urgency.> [!NOTE]
>
> The component calls the model once for each input row, except rows whose selected inputs are all NULL, so running it on large tables can incur significant inference costs. Usage is also subject to Google BigQuery's [generative AI quotas](https://docs.cloud.google.com/bigquery/quotas#generative_ai_functions). To view these charges, read [Track costs](https://docs.cloud.google.com/bigquery/docs/generative-ai-overview#track_costs).***## PrerequisitesThe Google Cloud service account that calls the Gemini model must have the [Agent Platform User](https://docs.cloud.google.com/iam/docs/roles-permissions/aiplatform#aiplatform.user) role (`roles/aiplatform.user`). Which service account this is depends on the **Agent platform connection** property:- **Agent platform connection left blank:** Google BigQuery calls the model as the Google Cloud service account that your Maia environment uses to access Google BigQuery. This is the service account in the [cloud provider credentials](https://docs.maia.ai/docs/guides/cloud-credentials.md) associated with the environment or, for a [Hybrid SaaS](https://docs.maia.ai/docs/guides/hybrid-saas-bigquery.md) environment with no associated credentials, the Maia runner's own service account.
- **Agent platform connection set:** Google BigQuery calls the model as the service account of the Google BigQuery connection. To create a connection and grant its service account the required role, read [Run generative AI queries with a Google BigQuery Connection](https://docs.cloud.google.com/bigquery/docs/permissions-for-ai-functions#run-connect).***## Properties**Name** `string` *required*A human-readable name for the component.**Model** `drop-down` *required*Select the Gemini model to use. The following options are currently available:- `gemini-3.5-flash`
- `gemini-3.1-flash-lite`
- `[Custom]`: Use a Gemini model that isn't listed here, which you name in **Custom model**.Google serves `gemini-3.5-flash` and `gemini-3.1-flash-lite` only from multi-regional endpoints. Read the [AI.SCORE](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-score#arguments) documentation to learn which endpoint Google BigQuery uses for the location your query runs in.**Custom model** `string` *required*The name of the Gemini model to use—for example, `gemini-3.7-flash`. Read the [AI.SCORE](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-score#arguments) documentation for the Gemini models that Google BigQuery supports.This property is only available when **Model** is set to **\[Custom]**.**Scoring criteria** `text editor` *required*The criteria that the model rates each row against—for example, `Rate how positive this review is, from 1 to 10`. The score has no fixed range, so state the range you want in your criteria.**Inputs** `column editor` *required*Select the source columns whose values are sent to the model with the scoring criteria. Select at least one column.- **Column Name:** A column from the input table.
- **Descriptive Name (Optional):** An alternate descriptive name to better contextualize the column. Recommended if your column names are low-context.The component adds a colon and a blank line after your scoring criteria, then sends each input as a `#<name>` heading followed by its value, with a blank line between inputs. `<name>` is the descriptive name if you've set one, or the column name otherwise. For example:```text theme={null}
Rate how positive this review is, from 1 to 10:

#Product
Deluxe Kettle

#Review
Boils quickly and looks great on the counter.
```A NULL input value is sent as empty text, except in `STRUCT`, `ARRAY`, and `JSON` columns, where it's sent as `null`. Input values that aren't text are converted to text before they're sent: `STRUCT`, `ARRAY`, and `JSON` values are converted to JSON text, and other data types are cast to `STRING`. `GEOGRAPHY` columns aren't supported.Toggle **Text mode** on at the bottom of the dialog to open a multi-line editor that lets you add items in a single block. For more information, read [Text mode](https://docs.maia.ai/docs/guides/components-overview.md#text-mode).To use grid variables, toggle **Use Grid Variable** on. For more information, read [Grid variables](https://docs.maia.ai/docs/guides/grid-variables.md).**Agent platform connection** `string`The Google BigQuery connection to use to call the model, in the format `<project_id>.<location>.<connection_id>`—for example, `myproject.us.myconnection`. Leave this blank to call the model as the Google Cloud service account that your environment uses to access Google BigQuery.**Include input columns** `boolean` *required*- **Yes:** Outputs both your source input columns *and* the new score column. This also includes input columns that aren't selected in **Inputs**. This is the default setting.
- **No:** Only outputs the new score column.**Result column name** `string` *required*The name of the column that holds the score for each row. The default is `score`.When **Include input columns** is set to **Yes**, the name must be different from the names of the input columns, ignoring case.## Explanation of outputThe output contains the input columns (if **Include input columns** is set to **Yes**), followed by the score column, named by **Result column name**. The score column contains a `FLOAT64` value.If every selected input is NULL for a row, that row's score is NULL.A row whose model call fails doesn't stop the run. Its score is NULL, and the output doesn't show why the call failed, so a failed row looks the same as a row whose selected inputs are all NULL. The run fails only if the model call fails for every row.


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