Unstructured

Go to https://platform.unstructured.io and use your email address, Google account, or GitHub account to sign up for an Unstructured account (if you do not already have one) and sign into the account at the same time. The Unstructured user interface (UI) appears, and you can start using it right away.

1. In the Unstructured UI, on the sidebar, click Workflows.

   

2. Click New Workflow.

3. Select Build it Myself, if it is not already selected.

4. Click Continue. The visual workflow editor appears.

   

   The workflow is represented visually as a series of directed acyclic graph (DAG) nodes. Each node represents a step in the workflow. The workflow proceeds end to end from left to right. By default, the workflow starts with three nodes:

   • Source: This node represents the location where you have your files or data for Unstructured to process. For this quickstart, this node represents a single file on your local machine. After you get the results you want, you can update this node to represent files or data in a remote location at scale in production.
   • Partitioner: This node represents the partitioning step, which extracts content from unstructured files and data and outputs it as structured document elements for consistent representation across varying kinds of file and data types. For this quickstart, this node extracts the contents of a single file on your local machine and outputs it as a series of structured document elements in JSON format.
   • Destination: This node represents the location where you want Unstructured to put the processed files or data. After you get the results you want, you can update this node to have Unstructured put the processed files or data into a remote location at scale in production.

1. Drag the file that you want Unstructured to process from your local machine’s file browser app and drop it into the Source node’s Drop file to test area. The file must have a size of 10 MB or less and one of the file types listed at the beginning of this quickstart.

   If you are not able to drag and drop the file, you can click Drop file to test and then browse to and select the file instead.

   Alternatively, you can use a sample file that Unstructured offers. To do this, click the Source node, and then in the Source pane, with Details selected, on the Local file tab, click one of the files under Or use a provided sample file. To view the file’s contents before you select it, click the eyes button next to the file.

2. Above the Source node, click Test.

   

   Unstructured displays a visual representation of the file and begins processing its contents, sending it through each of the workflow’s nodes in sequence. Depending on the file’s size and the workflow’s complexity, this processing could take several minutes.

   After Unstructured has finished its processing, the processed data appears in the Test output pane, as a series of structured elements in JSON format.

   

3. In the Test output pane, you can:

   • Search through the processed, JSON-formatted representation of the file by using the Search JSON box.
   • Download the full JSON as a .json file to your local machine by clicking Download full JSON.

4. When you are done, click the Close button in the Test output pane.

1. You can now add more nodes to the workflow to do further testing of various Unstructured features and with the option of eventually moving the workflow into production. For example, you can:

   

   • Add a Chunker node after the Partitioner node, to chunk the partitioned data into smaller pieces for your retrieval augmented generation (RAG) applications. To do this, click the add (+) button to the right of the Partitioner node, and then click Enrich > Chunker. Click the new Chunker node and specify its settings. For help, click the FAQ button in the Chunker node’s pane. Learn more about chunking and chunker settings.
   • Add an Enrichment node after the Chunker node, to apply enrichments to the chunked data such as image summaries, table summaries, table-to-HTML transforms, and named entity recognition (NER). To do this, click the add (+) button to the right of the Chunker node, and then click Enrich > Enrichment. Click the new Enrichment node and specify its settings. For help, click the FAQ button in the Enrichment node’s pane. Learn more about enrichments and enrichment settings.
   • Add an Embedder node after the Enrichment node, to generate vector embeddings for performing vector-based searches. To do this, click the add (+) button to the right of the Enrichment node, and then click Transform > Embedder. Click the new Embedder node and specify its settings. For help, click the FAQ button in the Embedder node’s pane. Learn more about embedding and embedding settings.

2. Each time you add a node or change its settings, you can click Test above the Source node again to test the current workflow end to end and see the results of the changes, if any.

3. Keep repeating this step as many times as you want, until you get the results you want.

After you get the results you want, you have the option of moving your workflow into production. To do this, complete the following instructions.

1. With your workflow remaining open in the visual workflow editor, open a new tab in your web browser, and in this new tab, sign in to your Unstructured account, at https://platform.unstructured.io.

2. In this new tab, create a source connector for your remote source location. This is the location in production where you have files or data in a file or object store, website, database, or vector store that you want Unstructured to process.

   

3. Create a destination connector for your remote destination location. This is the location in production where you want Unstructured to put the processed data as .json files in a file or object store, or as records in a database or vector store.

4. Create a workflow: on the sidebar, click Workflows, and then click New Workflow. Select Build it Myself, and then click Continue to open the visual workflow editor.

5. In the visual workflow editor, click Source.

6. In the Source pane, with Details selected, on the Connectors tab, select the source connector that you just created.

7. Click the Destination node.

8. In the Destination pane, with Details selected, select the destination connector that you just created.

9. Using your original workflow on the other tab as a guide, add any additional nodes to this new workflow as needed, and configure those new nodes’ settings to match the other ones.

10. Click Save.

11. To run the workflow:

    a. Make sure to click Save first.
    b. Click the Close button next to the workflow’s name in the top navigation bar.
    c. On the sidebar, click Workflows.
    d. In the list of available workflows, click the Run button for the workflow that you just saved.
    e. On the sidebar, click Jobs.
    

    

    f. In the list of available jobs, click the job that you just ran.
    g. After the job status shows Finished, go to the your destination location to see the processed files or data that Unstructured put there.

See also the remote quickstart for more coverage about how to set up and run production-ready Unstructured ETL+ workflows at scale.

1. Go to https://platform.unstructured.io and use your email address, Google account, or GitHub account to sign up for an Unstructured account (if you do not already have one) and sign into the account at the same time. The Unstructured user interface (UI) appears.

2. Get your Unstructured API key:

   a. In the Unstructured UI, click API Keys on the sidebar.
   b. Click Generate API Key.
   c. Follow the on-screen instructions to finish generating the key.
   d. Click the Copy icon next to your new key to add the key to your system’s clipboard. If you lose this key, simply return and click the Copy icon again.
   

This quickstart uses an Amazon S3 bucket as both the source location and the destination location. (You can use other source and destination types that are supported by Unstructured. If you use a different source or destination type, or if you use a different S3 bucket for the destination location, you will need to modify the quickstart notebook accordingly.)

Inside of the S3 bucket, a folder named input represents the source location. This is where your files to be processed will be stored. The S3 URI to the source location will be s3://<your-bucket-name>/input.

Inside of the same S3 bucket, a folder inside named output represents the destination location. This is where Unstructured will put the processed data. The S3 URI to the destination location will be s3://<your-bucket-name>/output.

Learn how to create an S3 bucket and set it up for Unstructured. (Do not run the Python SDK code or REST commands at the end of those setup instructions.)

After your S3 bucket is created and set up, follow the instructions in this quickstart notebook.

After you run the quickstart notebook, go to your destination location to view the processed data.