For the latest approach, go to: v2
The unstructured library includes a CLI to batch ingest documents from 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 \
--remote-url s3://utic-dev-tech-fixtures/small-pdf-set/ \
--anonymous \
--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.
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 \
--remote-url s3://utic-dev-tech-fixtures/small-pdf-set/ \
--anonymous \
--output-dir s3-small-batch-output \
--num-processes 2
To add a connector, refer to unstructured_ingest/connector/github.py as an example that implements the three relevant abstract base classes.
If the connector has an available fsspec
implementation, then refer to unstructured_ingest/connector/s3.py.
Then, update unstructured_ingest/main.py/cli to add a subcommand associated with the connector, and hook it up to the parent group.
Add an implementation of BaseRunner
in the runner directory to connect the invocation of the CLI with the underlying connector created.
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.
To add a destination connector, refer to unstructured_ingest/connector/delta-table.py as an example, which extends the BaseDestinationConnector
, and the WriteConfig
. It also shows how an existing data provider can be used for both a source and destination connector.
Similar to the runner used to connect source connectors with the CLI, destination connectors require an entry in the writer map defined in unstructured_ingest/runner/writers.py. This allows any source connector to use any destination connector.
Regarding the entry in the CLI, destination connectors are exposed as a subcommand that gets added to each source connector parent command. Special care needs to be taken here to not break the code being run by the source connector. Take a look at how the base runner class is dynamically pulled using the name of the parent CLI command in unstructured_ingest/cli/cmds/delta_table.py.
Similar tests and examples should be added to demonstrate/validate the use of the destination connector similar to the steps laid out for a source connector.
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/github.py.
- The subclass of
BaseIngestDoc
overridesprocess_file()
if extra processing logic is needed other than what is provided by auto.partition(). - If the IngestDoc relies on a connection or session that could be reused, the subclass of
BaseConnectorConfig
implements a session handle to manage connections. The ConnectorConfig subclass should also inherit fromConfigSessionHandleMixin
and the IngestDoc subclass should also inherit fromIngestDocSessionHandleMixin
. Check here for a detailed example. - The subclass of
BaseIngestDoc
implements relevant data source properties to include metadata. Check this PR for detailed examples.- The field
record_locator
property should include all of the information required to be able to reach to the document in the source platform.
- The field
- Add the relevant decorators from
unstructured.ingest.error
on top of relevant methods to handle errors such as a source connection error, destination connection error, or a partition error. For examples, check here.
- The subclass of
- Update unstructured_ingest/cli 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.
- Make sure the tests for the connector are running and not skipped by reviewing the logs in CI.
- 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 anotherpip-compile
line to thepip-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. forGitHubConnector
should look like@requires_dependencies(dependencies=["github"], extras="github")
- Run
make tidy
andmake check
to ensure linting checks pass.
- Update ingest documentation here
- For team members that are developing in the original repository:
- If there are secret variables created for the connector tests, make sure to:
- add the secrets into Github (contact someone with access)
- include the secret variables in
ci.yml
andingest-test-fixtures-update-pr.yml
- add a make install line in the workflow configurations to be able to provide the workflow machine with the required dependencies on the connector while testing
- Whenever necessary, use the ingest update test fixtures workflow to update the test fixtures.
- If there are secret variables created for the connector tests, make sure to:
- 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 toMyIngestDoc.has_output()
which is invoked in MainProcess._filter_docs_with_outputs. - Unless
.reprocess
isTrue
, then documents are always reprocessed. - If
.preserve_download
isTrue
, documents downloaded to.download_dir
are not removed after processing. - Else if
.preserve_download
isFalse
, documents downloaded to.download_dir
are removed after they are successfully processed during the invocation ofMyIngestDoc.cleanup_file()
in process_document - Does not re-download documents to
.download_dir
if.re_download
is False, enforced inMyIngestDoc.get_file()
- Prints more details if
--verbose
in ingest CLI, similar to unstructured_ingest/connector/github.py logging messages.
- If running with an
unstructured_ingest/main.py
is the entrypoint for the unstructured-ingest
cli. It calls the cli Command as fetched from cli.py
get_cmd()
.
The ingest directory is broken up in such a way that most of the code can be used with or without invoking the CLI itself:
- Connector: This houses the main code that is responsible for reaching out to external data providers and pulling down the data (i.e. S3, Azure, etc)
- Runner: This serves as the interface between the CLI specific commands and running the connector code. A base runner class exists that defines much of the common functionality across all connectors and allowed for typed methods to be defined to explicitly connect the CLI command to the specific connector.
- CLI: This is where the
Click
python library is introduced to create the cli bindings that a user interacts with then invoking the CLI directly. Many of the common options across commands are abstracted away and add options dynamically to click commands.
The ingest flow is similar to an ETL pipeline that gets defined at runtime based on user input:
Each step in the pipeline caches the results in a default location if one is not provided to it. This allows for the pipeline to pick up where it ended if an error occurred before it finished without having to recompute everything that ran successfully. It uses a hash of the parameters passed in for each step along with the previous step to know if the results it already has are still valid or should be recomputed regardless of them existing already. This allows you to change parameters associated with a step in the tail end of the pipeline and it only recomputes from there.
Multiprocessing: One of the options for the pipeline is how many processes to use. Not all steps support multiprocessing, but if they do, a multiprocessing Pool is used to speed up the process. For debugging purposes, if a single process is set, multiprocessing Pool isn't used at all.
While all the configurations are added to a single Click command when the CLI is invoked as options, many of these are bundled together based on a particular step in the pipeline. A BaseConfig
is extended in the root interfaces file and then that can be extended once again in the cli-specific interfaces file which adds a function on how the fields in the base config should be mapped to Click
options.
PartitionConfig
: Data associated with running the partitioning over the files pulled down via the source connector.ProcessorConfig
: Data around the process as a whole, such as number of processes to use when running, where to store the final result of the pipeline, and if an error should be raised if a single doc fails. By default, the pipeline will continue with that it can, so if a doc fails out of many, an error will be logged and the rest will continue.ReadConfig
: Data associated with pulling the data from the source data provider, such as if it should be redownloaded, regardless of the files already existing.EmbeddingConfig
: Data associated with running an optional embedder on the data, which adds a new field to the output json for each element with it's associated embeddings vector.ChunkingConfig
: Data associated with running an optional chunker over the partitioned data.PermissionsConfig
: Data associated with pulling down permissions data (i.e. RBAC). This is an optional feature and if enabled, will append the information pulled down to the metadata associated with an element.WriteConfig
: Any specific data needed to write to a destination connector. This does not have to be used if not needed.
For the flow of the pipeline, the only required steps are:
- Doc Factory: This creates instances of
BaseIngestDoc
which provide references to a file on the source data provider without downloading anything yet. - Source Node: This is responsible for downloading and content and producing a representation of that content suitable for partitioning.
- Partitioner: Responsible for running partition over the content produced by the previous source node.
Optional Steps:
- Reformat Nodes: Any number of reformat nodes can be set to modify the partitioned content. Currently chunking and embedding are supported.
- Write Node: If set, write the results to a destination via a destination connector.
Because there can be any number of reformat nodes, the final destination is not deterministic, so an extra step is added at the end of all reformat nodes to copy the final result to the location the user expects it to be when the pipeline ends.