ETL plus for GenAI data banner Unstructured provides a platform and tools to ingest and process unstructured documents for retrieval-augmented generation (RAG), agentic AI, and model fine-tuning. This 60-second video describes more about what Unstructured does and its benefits: This 40-second video demonstrates a simple use case that Unstructured helps solve: This one-minute video shows why using Unstructured is preferable to building your own similar solution: Unstructured offers the Unstructured user interface (UI) and the Unstructured API. Read on to learn more.

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Unstructured user interface (UI)

No-code UI. Production-ready. Learn more. Here is a screenshot of the Unstructured UI Start page: Partial view of the Unstructured UI This 90-second video provides a brief overview of the Unstructured UI:
To start using the Unstructured UI right away, skip ahead to the quickstart.

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Unstructured API

Use scripts or code. Production-ready. Learn more. The Unstructured API consists of two parts:
  • The Unstructured Workflow Endpoint enables a full range of partitioning, chunking, embedding, and enrichment options for your files and data. It is designed to batch-process files and data in remote locations; send processed results to various storage, databases, and vector stores; and use the latest and highest-performing models on the market today. It has built-in logic to deliver the highest quality results at the lowest cost. Learn more.
  • The Unstructured Partition Endpoint is intended for rapid prototyping of Unstructured’s various partitioning strategies, with limited support for chunking. It is designed to work only with processing of local files, one file at a time. Use the Unstructured Workflow Endpoint for production-level scenarios, file processing in batches, files and data in remote locations, generating embeddings, applying post-transform enrichments, using the latest and highest-performing models, and for the highest quality results at the lowest cost. Learn more.
Here is a screenshot of some Python code that calls the Unstructured Workflow Endpoint: Python code that calls the Unstructured Workflow Endpoint
To start using the Unstructured Workflow Endpoint right away, skip ahead to the quickstart.

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Supported file types

The Unstructured user interface (UI) and Unstructured API support processing of the following file types: By file extension:
File extension
.abw
.bmp
.csv
.cwk
.dbf
.dif*
.doc
.docm
.docx
.dot
.dotm
.eml
.epub
.et
.eth
.fods
.heic
.htm
.html
.hwp
.jpeg
.jpg
.md
.mcw
.msg
.mw
.odt
.org
.p7s
.pbd
.pdf
.png
.pot
.ppt
.pptm
.pptx
.prn
.rst
.rtf
.sdp
.sxg
.tiff
.txt
.tsv
.xls
.xlsx
.xml
.zabw
By file type:
CategoryFile types
Apple.cwk, .mcw
CSV.csv
Data Interchange.dif*
dBase.dbf
E-mail.eml, .msg, .p7s
EPUB.epub
HTML.htm, .html
Image.bmp, .heic, .jpeg, .jpg, .png, .prn, .tiff
Markdown.md
OpenOffice.odt
Org Mode.org
Other.eth, .pbd, .sdp
PDF.pdf
Plain text.txt
PowerPoint.pot, .ppt, .pptm, .pptx
reStructured Text.rst
Rich Text.rtf
Spreadsheet.et, .fods, .mw, .xls, .xlsx
StarOffice.sxg
TSV.tsv
Word processing.abw, .doc, .docm, .docx, .dot, .dotm, .hwp, .zabw
XML.xml
* For .dif, \n characters in .dif files are supported, but \r\n characters will raise the error UnsupportedFileFormatError: Partitioning is not supported for the FileType.UNK file type.

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Pricing

To use the Unstructured UI or call the Unstructured API, you must have an Unstructured account. Unstructured offers three account pricing plans:
  • SaaS Cloud-hosted - Processing happens on Unstructured’s software-as-a-service (SaaS) cloud infrastructure in a multi-tenant environment.
  • Private SaaS - Processing also happens on Unstructured’s SaaS cloud infrastructure, but your data stays protected in a dedicated cloud environment, maintaining strict data privacy.
  • VPC - Sometimes referred to as self-hosted, an instance of the Unstructured SaaS is deployed into your own virtual private cloud (VPC), providing complete data ownership and infrastructure control, full customization, and dedicated technical support.
For more details, see the Unstructured Pricing page. Some of these plans are billed on a per-page basis. Unstructured calculates a page as follows:
  • For these file types, a page is a page, slide, or image: .pdf, .pptx, and .tiff.
  • For .docx files that have page metadata, Unstructured calculates the number of pages based on that metadata.
  • For all other file types, Unstructured calculates the number of pages as the file’s size divided by 100 KB.
  • For non-file data, Unstructured calculates a page as 100 KB of incoming data to be processed.

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Unstructured UI quickstart

This quickstart uses a no-code, point-and-click user interface (UI) in your web browser to have Unstructured process a single file that is stored on your local machine. The file is first processed on Unstructured-hosted compute resources. The UI then shows the processed data that Unstructured generates for that file. You can download that processed data as a .json file to your local machine. This approach enables rapid, local, run-adjust-repeat prototyping of end-to-end Unstructured ETL+ workflows with a full range of Unstructured features. After you get the results you want, you can then attach remote source and destination connectors to both ends of your existing workflow to begin processing remote files and data at scale in production. To run this quickstart, you will need a local file with a size of 10 MB or less and one of the following file types:
File type
.bmp
.csv
.doc
.docx
.email
.epub
.heic
.html
.jpg
.md
.odt
.org
.pdf
.pot
.potm
.ppt
.pptm
.pptx
.rst
.rtf
.sgl
.tiff
.txt
.tsv
.xls
.xlsx
.xml
For processing remote files at scale in production, Unstructured supports many more files types than these. See the list of supported file types.Unstructured also supports processing files from remote object stores, and data from remote sources in websites, web apps, databases, and vector stores. For more information, see the source connector overview and the remote quickstart for how to set up and run production-ready Unstructured ETL+ workflows at scale.
If you do not have any files available, you can use one of the sample files that Unstructured offers in the UI. Or, you can download one or more sample files from the example-docs folder in the Unstructured repo on GitHub.
1

Sign up and sign in

  • If you do not already have an Unstructured account, go to https://unstructured.io/contact and fill out the online form to indicate your interest.
  • If you already have an Unstructured account, sign in by using the URL of the sign in page that Unstructured provided to you when your Unstructured account was created. After you sign in, the Unstructured user interface (UI) then appears, and you can start using it right away. If you do not have this URL, contact Unstructured Sales at sales@unstructured.io.
2

Create a workflow

  1. In the Unstructured UI, on the sidebar, click Workflows. Workflows in the sidebar
  2. Click New Workflow.
  3. Select Build it Myself, if it is not already selected.
  4. Click Continue. The visual workflow editor appears. Visual workflow designer 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.
3

Process a local file

  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. Testing a single local file workflow 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. Viewing single local file output
  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.
4

Add more nodes to the workflow

  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: Adding a node to the workflow
    • 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.
      Image summary descriptions, table summary descriptions, and table-to-HTML output is generated only when the Partitioner node in a workflow is set to use the High Res partitioning strategy and the workflow also contains an image description, table description, or table-to-HTML enrichment node.Setting the Partitioner node to use Auto, VLM, or Fast in a workflow that also contains an image description, table description, or table-to-HTML enrichment node will not generate any image summary descriptions, table summary descriptions, or table-to-HTML output, and it could also cause the workflow to stop running or produce unexpected results.
    • 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.
5

Next steps

After you get the results you want, you have the option of moving your workflow into production. To do this, complete the following instructions.
The following instructions have you create a new workflow that is suitable for production. This behavior is planned to be fixed in a future release, allowing you to update the workflow that you just created, rather than needing to create a new one.
  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:
    • If you do not already have an Unstructured account, go to https://unstructured.io/contact and fill out the online form to indicate your interest.
    • If you already have an Unstructured account, sign in by using the URL of the sign in page that Unstructured provided to you when your Unstructured account was created. If you do not have this URL, contact Unstructured Sales at sales@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. Connectors button on the sidebar
  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.
     Viewing the list of available 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.

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Unstructured Workflow Endpoint quickstart

This quickstart uses the Unstructured Python SDK to call the Unstructured Workflow Endpoint to get your data RAG-ready. The Python code for this quickstart is in a remote hosted Google Colab notebook. Data is processed on Unstructured-hosted compute resources. The requirements are as follows:
  • A compatible source (input) location that contains your data for Unstructured to process. See the list of supported source types. This quickstart uses an Amazon S3 bucket as the source location. If you use a different source type, you will need to modify the quickstart notebook accordingly.
  • For document-based source locations, compatible files in that location. See the list of supported file types. If you do not have any files available, you can download some from the example-docs folder in the Unstructured-IO/unstructured-ingest repository in GitHub.
  • A compatible destination (output) location for Unstructured to put the processed data. See the list of supported destination types. For this quickstart’s destination location, a different folder in the same Amazon S3 bucket as the source location is used. If you use a different destination S3 bucket or a different destination type, you will need to modify the quickstart notebook accordingly.
1

Sign up, sign in, and get your API key

  1. Sign in to your Unstructured account:
    • If you do not already have an Unstructured account, go to https://unstructured.io/contact and fill out the online form to indicate your interest.
    • If you already have an Unstructured account, sign in by using the URL of the sign in page that Unstructured provided to you when your Unstructured account was created. After you sign in, the Unstructured user interface (UI) then appears, and you can start using it right away. If you do not have this URL, contact Unstructured Sales at sales@unstructured.io.
  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.
2

Create and set up the S3 bucket

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.)
3

Run the quickstart notebook

After your S3 bucket is created and set up, follow the instructions in this quickstart notebook.
4

View the processed data

After you run the quickstart notebook, go to your destination location to view the processed data.
Learn more about the Unstructured API.

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Get in touch

If you can’t find the information you’re looking for in the documentation, or if you need help, contact us directly, or join our Slack where our team and community can help you.