Quickstart
Work with your unstructured data at scale in under 5 minutes
Upload Your Unstructured Data
Navigate to the Roe Dataset page
![](https://mintlify.s3-us-west-1.amazonaws.com/roeai/images/database-quickstart-roe-dataset.png)
Upload a file to the default dataset
For the purpose of this quickstart, we will use the same invoice PDF file as invoice.pdf
file in the invoice-extraction-example
dataset. Go to that dataset and download the file first. Then upload to the default
dataset.
![](https://mintlify.s3-us-west-1.amazonaws.com/roeai/images/database-quickstart-upload-file-1.png)
View the default dataset
![](https://mintlify.s3-us-west-1.amazonaws.com/roeai/images/database-quickstart-upload-file-2.png)
Upload your files
View your uploaded files
![](https://mintlify.s3-us-west-1.amazonaws.com/roeai/images/database-quickstart-uploaded-file.png)
Once uploaded, your files will be listed here
View Your Data
Click on the Workspace tab to go to VolansDB.
A default table called dataset_default
has been created for you. Any new
files uploaded to the default
dataset will automatically be added to this table as well. You can check the table by clicking dataset_default
table in the table list.
![](https://mintlify.s3-us-west-1.amazonaws.com/roeai/images/database-quickstart-default-table.png)
Or you can run the following SQL query to view the data in a new worksheet tab:
SELECT * FROM dataset_default;
![](https://mintlify.s3-us-west-1.amazonaws.com/roeai/images/database-quickstart-select-default-table.png)
Process Your Data
Now that you have uploaded your data, you can start processing it using the built-in functions. For example, you can extract information from the invoice PDF with the following SQL query:
-- You're shown three example worksheets for three different use cases. Take a moment to read through each one!
-- This worksheet extracts information from the invoice PDF as specified by the return format requested. Try running it!
SELECT name, file, extract_from(
'returns following structure: {
from: <from company name>,
recipient: <recipient company name>,
line_items: [
{
name: <item name>
quantity: <item quantity>
cost: <item total cost>
},
<more line items>
],
subtotal: <amount before tax>
tax: <tax amount>
total: <total amount due>
}', -- extract_from() uses the Default Extraction agent, so you don't need to specify an agent
file
) FROM dataset_examples WHERE dataset='invoice-extraction-example'; -- You can run on all datasets in the table or a specific one (what we do here)
The result of the extract_from
function is a reference to the async job. Once
the job is completed, you can view the extracted data in the worksheet by clicking on the Job ID.
![](https://mintlify.s3-us-west-1.amazonaws.com/roeai/images/database-quickstart-result.png)
Next Steps
Learn More About VolansDB SQL
We are based on Clickhouse, so you can use the full power of Clickhouse SQL to process your data. You can learn more about Clickhouse SQL in their official documentation.
Also check out our examples to see how you can use SQL to process your data.
Learn More About VolansDB Functions
extract_from
is just one of the many functions you can use to process your
data. We have a variety of functions available to help you process your data
with VolansDB Agents. You can learn more about the functions available in the
Functions section.