> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify-poc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# vectorizer_status

> View to monitor vectorizer state and pending items

Get a high-level overview of all vectorizers in the system.

* Regularly monitor and check the health of the entire system
* Display key information about each vectorizer's configuration and current state
* Use the `pending_items` column to get a quick indication of processing backlogs

## Samples

### Retrieve all vectorizers with pending items

```sql theme={"dark"}
SELECT * FROM ai.vectorizer_status WHERE pending_items > 0;
```

### System health monitoring

Alert if any vectorizer has more than 1000 pending items.

```sql theme={"dark"}
SELECT id, source_table, pending_items
FROM ai.vectorizer_status
WHERE pending_items > 1000;
```

### Get overview of all vectorizers

```sql theme={"dark"}
SELECT * FROM ai.vectorizer_status;
```

Sample output:

| id | source\_table | target\_table                           | view                              | pending\_items |
| -- | ------------- | --------------------------------------- | --------------------------------- | -------------- |
| 1  | public.blog   | public.blog\_contents\_embedding\_store | public.blog\_contents\_embeddings | 1              |

## Returns

| Column name    | Description                                                           |
| -------------- | --------------------------------------------------------------------- |
| id             | The unique identifier of this vectorizer                              |
| source\_table  | The fully qualified name of the source table                          |
| target\_table  | The fully qualified name of the table storing the embeddings          |
| view           | The fully qualified name of the view joining source and target tables |
| pending\_items | The number of items waiting to be processed by the vectorizer         |

The `pending_items` column indicates the number of items still awaiting embedding creation. The pending items count helps you to:

* Identify bottlenecks in processing
* Determine if you need to adjust scheduling or processing configurations
* Monitor the impact of large data imports or updates on your vectorizers