This repo implements a pre-processing pipeline for the following documents. Currently, the pipeline is capable of recognizing the file type and choosing the relevant partition function to process the file.

Try our hosted API! It's freely available to use with any of the filetypes listed above. This is the easiest way to get started. If you'd like to host your own version of the API, jump down to the Developer Quickstart Guide.

 curl -X 'POST' \
  'https://api.unstructured.io/general/v0/general' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'files=@sample-docs/family-day.eml' \
  | jq -C . | less -R

Four strategies are available for processing PDF/Images files: hi_res, fast, ocr_only and auto. fast is the default strategy and works well for documents that do not have text embedded in images.

On the other hand, hi_res is the better choice for PDFs that may have text within embedded images, or for achieving greater precision of element types in the response JSON. Please be aware that, as of writing, hi_res requests may take 20 times longer to process compared to the fast option. See the example below for making a hi_res request.

 curl -X 'POST' \
  'https://api.unstructured.io/general/v0/general' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'files=@sample-docs/layout-parser-paper.pdf' \
  -F 'strategy=hi_res' \
  | jq -C . | less -R

The ocr_only strategy runs the document through Tesseract for OCR. Currently, hi_res has difficulty ordering elements for documents with multiple columns. If you have a document with multiple columns that do not have extractable text, we recommend using the ocr_only strategy. Please be aware that ocr_only will fall back to another strategy if Tesseract is not available.

For the best of all worlds, auto will determine when a page can be extracted using fast or ocr_only mode, otherwise it will fall back to hi_res.

You can also specify what languages to use for OCR with the ocr_languages kwarg. See the Tesseract documentation for a full list of languages and install instructions. OCR is only applied if the text is not already available in the PDF document.

curl -X 'POST' \
  'https://api.unstructured.io/general/v0/general' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'files=@sample-docs/english-and-korean.png' \
  -F 'strategy=ocr_only' \
  -F 'ocr_languages=eng'  \
  -F 'ocr_languages=kor'  \
  | jq -C . | less -R

When elements are extracted from PDFs or images, it may be useful to get their bounding boxes as well. Set the coordinates parameter to true to add this field to the elements in the response.

 curl -X 'POST' \
  'https://api.unstructured.io/general/v0/general' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'files=@sample-docs/layout-parser-paper.pdf' \
  -F 'coordinates=true' \
  | jq -C . | less -R

• Using pyenv to manage virtualenv's is recommended

  • Mac install instructions. See here for more detailed instructions.

    • brew install pyenv-virtualenv
    • pyenv install 3.8.17

  • Linux instructions are available here.

  • Create a virtualenv to work in and activate it, e.g. for one named document-processing:

    pyenv virtualenv 3.8.17 document-processing
pyenv activate document-processing

See the Unstructured Quick Start for the many OS dependencies that are required, if the ability to process all file types is desired.

• Run make install
• If image and high resolution pdf extraction is required, also run make install-high
• Start a local jupyter notebook server with make run-jupyter
  OR
  just start the fast-API locally with make run-web-app

After running make run-web-app (or make docker-start-api to run in the container), you can now hit the API locally at port 8000. The sample-docs directory has a number of example file types that are currently supported.

For example:

 curl -X 'POST' \
  'http:https://localhost:8000/general/v0/general' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'files=@sample-docs/family-day.eml' \
  | jq -C . | less -R

The response will be a list of the extracted elements:

[
  {
    "element_id": "db1ca22813f01feda8759ff04a844e56",
    "coordinates": null,
    "text": "Hi All,",
    "type": "UncategorizedText",
    "metadata": {
      "date": "2022-12-21T10:28:53-06:00",
      "sent_from": [
        "Mallori Harrell <[email protected]>"
      ],
      "sent_to": [
        "Mallori Harrell <[email protected]>"
      ],
      "subject": "Family Day",
      "filename": "family-day.eml"
    }
  },
...
...

The output format can also be set to text/csv to get the data in csv format rather than json:

 curl -X 'POST' \
  'http:https://localhost:8000/general/v0/general' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'files=@sample-docs/family-day.eml' \
  -F 'output_format="text/csv"'

The response will be a list of the extracted elements in csv format:

"type,text,element_id,filename,page_number,url,sent_from,sent_to,subject,sender\n
UncategorizedText,\"Hi,\",bc50944723f014607ad612b6983944a7,alert.eml,1,,['Mallori Harrell <[email protected]>'],['Mallori Harrell <[email protected]>'],ALERT: Stolen Lunch,Mallori Harrell <[email protected]>\n
NarrativeText,\"It has come to our attention that as of 9:00am this morning, Harold's lunch is missing. If this was done in error please return the lunch immediately to the fridge on the 2nd floor by noon.\",51944d1f63f9472edb165fb3c9e5c525,alert.eml,1,,['Mallori Harrell <[email protected]>'],['Mallori Harrell <[email protected]>'],ALERT: Stolen Lunch,Mallori Harrell <[email protected]>\n
NarrativeText,\"If the lunch has not been returned by noon, we will be reviewing camera footage to determine who stole Harold's lunch.\",8e8f9e2e50e39e072fda08d277aa77b9,alert.eml,1,,['Mallori Harrell <[email protected]>'],['Mallori Harrell <[email protected]>'],ALERT: Stolen Lunch,Mallori Harrell <[email protected]>\n
NarrativeText,The perpetrators will be PUNISHED to the full extent of our employee code of conduct handbook.,736a826679b971f594103fd9751e5c8f,alert.eml,1,,['Mallori Harrell <[email protected]>'],['Mallori Harrell <[email protected]>'],ALERT: Stolen Lunch,Mallori Harrell <[email protected]>\n
UncategorizedText,\"Thank you for your time,\",3eeae5f64dab54c52dd5fff779808071,alert.eml,1,,['Mallori Harrell <[email protected]>'],['Mallori Harrell <[email protected]>'],ALERT: Stolen Lunch,Mallori Harrell <[email protected]>\n
Title,Unstructured Technologies,d5b612de8cd918addd9569b0255b65b2,alert.eml,1,,['Mallori Harrell <[email protected]>'],['Mallori Harrell <[email protected]>'],ALERT: Stolen Lunch,Mallori Harrell <[email protected]>\n
Title,Data Scientist,46b174f1ec7c25d23e5e50ffff0cc55b,alert.eml,1,,['Mallori Harrell <[email protected]>'],['Mallori Harrell <[email protected]>'],ALERT: Stolen Lunch,Mallori Harrell <[email protected]>\n"

As mentioned above, processing a pdf using hi_res is currently a slow operation. One workaround is to split the pdf into smaller files, process these asynchronously, and merge the results. You can enable parallel processing mode with the following env variables:

• UNSTRUCTURED_PARALLEL_MODE_ENABLED - set to true to process individual pdf pages remotely
• UNSTRUCTURED_PARALLEL_MODE_URL - the location to send pdf page asynchronously

You can generate the FastAPI APIs from your pipeline notebooks by running make generate-api.

The following instructions are intended to help you get up and running using Docker to interact with unstructured-api. See here if you don't already have docker installed on your machine.

NOTE: we build multi-platform images to support both x86_64 and Apple silicon hardware. Docker pull should download the corresponding image for your architecture, but you can specify with --platform (e.g. --platform linux/amd64) if needed.

We build Docker images for all pushes to main. We tag each image with the corresponding short commit hash (e.g. fbc7a69) and the application version (e.g. 0.5.5-dev1). We also tag the most recent image with latest. To leverage this, docker pull from our image repository.

docker pull quay.io/unstructured-io/unstructured-api:latest

Once pulled, you can launch the container as a web app on localhost:8000.

docker run -p 8000:8000 -d --rm --name unstructured-api quay.io/unstructured-io/unstructured-api:latest --port 8000 --host 0.0.0.0

See our security policy for information on how to report security vulnerabilities.