> ## 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.

# loading_uri

> Load data to embed from a file referenced in a source table column

Load the data to embed from a file that is referenced in a column of the source table.

This file path is internally passed to [smart\_open][smartopen], so it supports any protocol that smart\_open supports, including:

* Local files
* Amazon S3
* Google Cloud Storage
* Azure Blob Storage
* HTTP/HTTPS
* SFTP
* and [many more][many-more]

## Environment configuration

Ensure the vectorizer worker has the correct credentials to access the file, such as in environment variables. Here is an example for AWS S3:

```bash theme={"dark"}
export AWS_ACCESS_KEY_ID='your_access_key'
export AWS_SECRET_ACCESS_KEY='your_secret_key'
export AWS_REGION='your_region'  # optional
```

Make sure these environment variables are properly set in the environment where the vectorizer worker runs.

## Samples

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'my_table'::regclass,
    loading => ai.loading_uri('file_uri_column_name'),
    -- other parameters...
);
```

## Arguments

| Name          | Type   | Default | Required | Description                                     |
| ------------- | ------ | ------- | -------- | ----------------------------------------------- |
| `column_name` | `TEXT` | -       | ✔        | The name of the column containing the file path |

## Returns

A JSON configuration object that you can use in `ai.create_vectorizer`.

[many-more]: https://github.com/piskvorky/smart_open/blob/master/help.txt

[smartopen]: https://github.com/piskvorky/smart_open