title | sidebarTitle |
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Unstructured |
Overview |
Unstructured provides a platform and tools to ingest and process unstructured documents for Retrieval Augmented Generation (RAG) and model fine-tuning.
This 40-second video demonstrates a simple use case that Unstructured helps solve:
<iframe width="560" height="315" src="https://www.youtube.com/embed/E-tupjji22U" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen ></iframe> Unstructured Platform - No-code UI. Production-ready. Pay as you go.
Unstructured Serverless API services - Use scripts or code. Production-ready. Pay as you go. (There is also a non-production, free edition with limits.)
Learn more about these products:
No-code user interface, pay-as-you-go platform to get all of your data RAG-ready.
Data is processed on Unstructured-hosted compute resources.
[Try the quickstart](#quickstart-unstructured-platform).
[Learn more](/platform/overview).
[Read the announcement](https://unstructured.io/blog/introducing-unstructured-platform).
Use scripts or code to call the Unstructured Ingest CLI or Ingest Python library, to get all of your data RAG-ready.
Unstructured Serverless API services have a [Serverless](api-reference/api-services/saas-api-development-guide) pay-as-you-go edition and a [Free](/api-reference/api-services/free-api_) [limited](/api-reference/api-services/free-api#free-unstructured-api-limitations) edition that process data on Unstructured-hosted compute resources.
If you need to use compute resources that you host instead, there are also [Azure](/api-reference/api-services/azure) pay-as-you-go and [AWS](/api-reference/api-services/aws) pay-as-you-go editions; these editions process data by using the Unstructured API installed on compute resources hosted in your own Azure or AWS account.
[Try the quickstart](#quickstart-unstructured-serverless-api).
[Learn more](/api-reference/api-services/overview).
[Read the launch announcement](https://unstructured.io/blog/introducing-unstructured-serverless-api).
import SupportedFileTypes from '/snippets/general-shared-text/supported-file-types.mdx';
If you want to use your local machine for either your source (input) files, or the destination (output) location for Unstructured to deliver the processed data, you cannot use this quickstart. You must run code on your local machine instead: skip to the Quickstart: Unstructured Serverless API, later in this article.
import SharedPlatform from '/snippets/quickstarts/platform.mdx';
Learn more about the Unstructured Platform.
import LocalToLocalPythonIngestLibrary from '/snippets/ingestion/local-to-local.v2.py.mdx'; import AdditionalIngestDependencies from '/snippets/general-shared-text/ingest-dependencies.mdx';
This quickstart uses your local machine, with the Unstructured Ingest Python library installed. It preprocesses source (input) files on your local machine, and it uses the Unstructured Serverless API to deliver the processed data to a destination (output) location, also on your local machine. Data is processed on Unstructured-hosted compute resources.
You will need:
- Python installed on your local machine.
- Compatible files on your local machine to be processed. 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 repo on GitHub.
- `<path/to/input>` with the source (input) path to the directory on your local machine that contains the compatible files for Unstructured to process on its hosted compute resources.
- `<path/to/output>` with the destination (output) path to the directory on your local machine that will contain the processed data that Unstructured returns from its hosted compute resources.
<LocalToLocalPythonIngestLibrary />
</Step>
<Step title="View the processed data">
Go to your destination location to view the processed data.
</Step>
Learn more about the Unstructured Serverless API.
If you can't find the information you're looking for in the documentation, or if you need help, get in touch with our Support team at [email protected], or join our Slack where our team and community can help you.