Batch Processing Documents

The unstructured-ingest CLI

The unstructured library includes a CLI to batch ingest documents from (soon to be various) sources, storing structured outputs locally on the filesystem.

For example, the following command processes all the documents in S3 in the utic-dev-tech-fixtures bucket with a prefix of small-pdf-set/.

unstructured-ingest \
   --s3-url s3://utic-dev-tech-fixtures/small-pdf-set/ \
   --s3-anonymous \
   --structured-output-dir s3-small-batch-output \
   --num-processes 2

Naturally, --num-processes may be adjusted for better instance utilization with multiprocessing.

Installation note: make sure to install the following extras when installing unstructured, needed for the above command:

pip install "unstructured[s3,local-inference]"

See the Quick Start which documents how to pip install dectectron2 and other OS dependencies, necessary for the parsing of .PDF files.

Developers' Guide

Local testing

When testing from a local checkout rather than a pip-installed version of unstructured, just execute unstructured/ingest/main.py, e.g.:

PYTHONPATH=. ./unstructured/ingest/main.py \
   --s3-url s3://utic-dev-tech-fixtures/small-pdf-set/ \
   --s3-anonymous \
   --structured-output-dir s3-small-batch-output \
   --num-processes 2

Adding Data Connectors

To add a connector, refer to unstructured/ingest/connector/s3_connector.py as example that implements the three relelvant abstract base classes.

Then, update unstructured/ingest/main.py to instantiate the connector specific to your class if its command line options are invoked.

Create at least one folder examples/ingest with an easily reproducible script that shows the new connector in action.

Finally, to ensure the connector remains stable, add a new script test_unstructured_ingest/test-ingest-<the-new-data-source>.sh similar to test_unstructured_ingest/test-ingest-s3.sh, and append a line invoking the new script in test_unstructured_ingest/test-ingest.sh.

You'll notice that the unstructured outputs for the new documents are expected to be checked into CI under test_unstructured_ingest/expected-structured-output/<folder-name-relevant-to-your-dataset>. So, you'll need to git add those json outputs so that test-ingest.sh passes in CI.

The main.py flags of --re-download/--no-re-download , --download-dir, --preserve-downloads, --structured-output-dir, and --reprocess are honored by the connector.

The checklist:

In checklist form, the above steps are summarized as:

Create a new module under unstructured/ingest/connector/ implementing the 3 abstract base classes, similar to unstructured/ingest/connector/s3_connector.py.
- The subclass of BaseIngestDoc overrides process_file() if extra processing logic is needed other than what is provided by auto.partition().
Update unstructured/ingest/main.py with support for the new connector.
Create a folder under examples/ingest that includes at least one well documented script.
Add a script test_unstructured_ingest/test-ingest-<the-new-data-source>.sh. It's json output files should have a total of no more than 100K.
Git add the expected outputs under test_unstructured_ingest/expected-structured-output/<folder-name-relevant-to-your-dataset> so the above test passes in CI.
Add a line to test_unstructured_ingest/test-ingest.sh invoking the new test script.
If additional python dependencies are needed for the new connector:
- Add them as an extra to setup.py.
- Update the Makefile, adding a target for install-ingest-<name> and adding another pip-compile line to the pip-compile make target. See this commit for a reference.
- The added dependencies should be imported at runtime when the new connector is invoked, rather than as top-level imports.
- Add the decorator unstructured.utils.requires_dependencies on top of each class instance or function that uses those connector-specific dependencies e.g. for S3Connector should look like @requires_dependencies(dependencies=["boto3"], extras="s3")
Honors the conventions of BaseConnectorConfig defined in unstructured/ingest/interfaces.py which is passed through the CLI:
- If running with an .output_dir where structured outputs already exists for a given file, the file content is not re-downloaded from the data source nor is it reprocessed. This is made possible by implementing the call to MyIngestDoc.has_output() which is invoked in MainProcess._filter_docs_with_outputs.
- Unless .reprocess is True, then documents are always reprocessed.
- If .preserve_download is True, documents downloaded to .download_dir are not removed after processing.
- Else if .preserve_download is False, documents downloaded to .download_dir are removed after they are successfully processed during the invocation of MyIngestDoc.cleanup_file() in process_document
- Does not re-download documents to .download_dir if .re_download is False, enforced in MyIngestDoc.get_file()
- Prints more details if .verbose similar to unstructured/ingest/connector/s3_connector.py.

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