Summary:
- bump unstructured-inference to `0.7.33`
- cut a release for `0.14.2`
- add some dependencies that previously came through from the
layoutparser extras.
### Summary
Switches to installing `libreoffice` from the Wolfi repository and
upgrades the `libreoffice` version to `libreoffice==24.x.x`. Resolves a
medium vulnerability in the old `libreoffice` version. Security scanning
with `anchore/grype` was also added to the `test_dockerfile` job.
Requirements were bumped to resolve a vulnerability in the `requests`
library.
### Testing
`test_dockerfile` passes with the updates.
### Summary
Closes#2959. Updates the dependency and CI to add support for Python
3.12.
The MongoDB ingest tests were disabled due to jobs like [this
one](https://github.com/Unstructured-IO/unstructured/actions/runs/9133383127/job/25116767333)
failing due to issues with the `bson` package. `bson` is a dependency
for the AstraDB connector, but `pymongo` does not work when `bson` is
installed from `pip`. This issue is documented by MongoDB
[here](https://pymongo.readthedocs.io/en/stable/installation.html). Spun
off #3049 to resolve this. Issue seems unrelated to Python 3.12, though
unsure why this didn't surface previously.
Disables the `argilla` tests because `argilla` does not yet support
Python 3.12. We can add the `argilla` tests back in once the PR
references below is merged. You can still use the `stage_for_argilla`
function if you're on `python<3.12` and you install `argilla` yourself.
- https://github.com/argilla-io/argilla/pull/4837
---------
Co-authored-by: Nicolò Boschi <boschi1997@gmail.com>
**Summary**
`unstructured` will use table features added in the most recent version
of `python-docx`.
Also update the `lxml` version constraint because `lxml>4.9.2` will not
install on Apple Silicon
(https://github.com/Unstructured-IO/unstructured/issues/1707).
`python-docx` requires `lxml` although other file formats require it as
well.
Cut a release.
Run pip-compile on mac to avoid `nvidia-*` requirements creeping into
`requirements/extra-pdf-image.txt`. This should fix arm64 image builds
that have been breaking on main.
### Description
* The `consistent-deps.sh` was fixed to take into account the ingest
dependencies, causing some errors to show up. New constriants were added
to make that script pass.
* Update all requirements without constraint on pydantic, allowing the
latest version to be pulled in.
* `pikepdf` is causing a conflict but there's a fix on their `main`
branch, just need for the next release to be published. Opened up a
question here to see if we can get that out any sooner: [Do releases
happen on a
schedule?](https://github.com/pikepdf/pikepdf/discussions/574). For now
added `lxml<5` to the constraints.
A couple optimizations:
* `constraints.in` renamed to `constraints.txt` since the whole point is
all dependencies are already pinned and the file never gets compiled
* `constraints.txt` moved to a `requirements/deps` directory as this
never gets compiled by `pip-compile`
* Other dependency files updated to reference the new location of
`base.in` and `constraints.txt`
* make file updated since it was originally written to avoid the
`base.in` and `constraints.in` file
Closes #2577
Testing:
```
from unstructured.partition.html import partition_html
cnn_lite_url = "https://lite.cnn.com/"
elements = partition_html(url=cnn_lite_url)
links = []
for element in elements:
if element.metadata.link_urls:
relative_link = element.metadata.link_urls[0][1:]
if relative_link.startswith("2024"):
links.append(f"{cnn_lite_url}{relative_link}")
print(links)
```
---------
Co-authored-by: ron-unstructured <ronny@unstructured.io>
Co-authored-by: Ronny H <138828701+ron-unstructured@users.noreply.github.com>
This PR:
- Moves ingest dependencies into local scopes to be able to import
ingest connector classes without the need of installing imported
external dependencies. This allows lightweight use of the classes (not
the instances. to use the instances as intended you'll still need the
dependencies).
- Upgrades the embed module dependencies from `langchain` to
`langchain-community` module (to pass CI [rather than introducing a
pin])
- Does pip-compile
- Does minor refactors in other files to pass `ruff 2.0` checks which
were introduced by pip-compile
.heic files are an image filetype we have not supported.
#### Testing
```
from unstructured.partition.image import partition_image
png_filename = "example-docs/DA-1p.png"
heic_filename = "example-docs/DA-1p.heic"
png_elements = partition_image(png_filename, strategy="hi_res")
heic_elements = partition_image(heic_filename, strategy="hi_res")
for i in range(len(heic_elements)):
print(heic_elements[i].text == png_elements[i].text)
```
---------
Co-authored-by: christinestraub <christinemstraub@gmail.com>
Replacement for #2311 since python 3.8 was dropped as a supported
version.
Unstructured-client added `api_key_auth` as a param to
`UnstructuredClient` in [version
0.9.0](8c93115c92).
This pins the version of `unstructured-client` so users do not receive
`TypeError: UnstructuredClient.__init__() got an unexpected keyword
argument 'api_key_auth'`
### Summary
This PR is the second part of the "image extraction" refactor to move it
from unstructured-inference repo to unstructured repo, the first part is
done in
https://github.com/Unstructured-IO/unstructured-inference/pull/299. This
PR adds logic to support extracting images.
### Testing
`git clone -b refactor/remove_image_extraction_code --single-branch
https://github.com/Unstructured-IO/unstructured-inference.git && cd
unstructured-inference && pip install -e . && cd ../`
```
elements = partition_pdf(
filename="example-docs/embedded-images.pdf",
strategy="hi_res",
extract_images_in_pdf=True,
)
print("\n\n".join([str(el) for el in elements]))
```
### Summary
This PR is the second part of `pdfminer` refactor to move it from
`unstructured-inference` repo to `unstructured` repo, the first part is
done in
https://github.com/Unstructured-IO/unstructured-inference/pull/294. This
PR adds logic to merge the extracted layout with the inferred layout.
The updated workflow for the `hi_res` strategy:
* pass the document (as data/filename) to the `inference` repo to get
`inferred_layout` (DocumentLayout)
* pass the `inferred_layout` returned from the `inference` repo and the
document (as data/filename) to the `pdfminer_processing` module, which
first opens the document (create temp file/dir as needed), and splits
the document by pages
* if is_image is `True`, return the passed
inferred_layout(DocumentLayout)
* if is_image is `False`:
* get extracted_layout (TextRegions) from the passed
document(data/filename) by pdfminer
* merge `extracted_layout` (TextRegions) with the passed
`inferred_layout` (DocumentLayout)
* return the `inferred_layout `(DocumentLayout) with updated elements
(all merged LayoutElements) as merged_layout (DocumentLayout)
* pass merged_layout and the document (as data/filename) to the `OCR`
module, which first opens the document (create temp file/dir as needed),
and splits the document by pages (convert PDF pages to image pages for
PDF file)
### Note
This PR also fixes issue #2164 by using functionality similar to the one
implemented in the `fast` strategy workflow when extracting elements by
`pdfminer`.
### TODO
* image extraction refactor to move it from `unstructured-inference`
repo to `unstructured` repo
* improving natural reading order by applying the current default
`xycut` sorting to the elements extracted by `pdfminer`
### Summary
Closes#2033
Updates `partition_via_api` to use `UnstructuredClient` for api calls
instead of `requests`.
Updates associated tests.
Note: This PR does **not** update `partition_multiple_via_api` as
documentation in `unstructured-python-client` indicates it does not
support multiple files. A new issue should be opened to add that
functionality to `unstructured-python-client`.
---------
Co-authored-by: Klaijan <klaijan@unstructured.io>
Co-authored-by: Roman Isecke <136338424+rbiseck3@users.noreply.github.com>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: rbiseck3 <rbiseck3@users.noreply.github.com>
### Summary
Add a procedure to repair PDF when the PDF structure is invalid for
`PDFminer` to process.
This PR handles two cases of `PSSyntaxError Invalid dictionary
construct: ...`:
* PDFminer open entire document and create pages generator on
`PDFPage.get_pages(fp)`: [sentry log
example](https://unstructuredio.sentry.io/issues/4655715023/?alert_rule_id=14681339&alert_type=issue¬ification_uuid=d8db4cf4-686f-4504-8a22-74a79a8e966f&project=4505909127086080&referrer=slack)
* PDFminer's interpreter process a single page on
`interpreter.process_page(page)`: [sentry log
example](https://unstructuredio.sentry.io/issues/4655898781/?referrer=slack¬ification_uuid=0d929d48-f490-4db8-8dad-5d431c8460bc&alert_rule_id=14681339&alert_type=issue)
**Additional tech details:**
* Add new dependency `pikepdf` in `requirements/extra-pdf-image.in`,
which is used for repairing PDF.
* Add new denpendenct `pypdf` in `requirements/extra-pdf-image.in`,
which is used to find the error page from entire document by reading the
PDF file again (can't find a way to split pdf in PDFminer).
* Refactor the `is null` check for `get_uris_from_annots`, since the
root cause is that `get_uris` passed a None `annots` to
`get_uris_from_annots`, so the Null check should happen in `get_uris`.
* Add more type protection in `get_uris_from_annots` when using any
`PDFObjRef.resolve()` as `dict` (it could still be a `PDFObjRef`). This
should fix :
* https://github.com/Unstructured-IO/unstructured/issues/1922 where
`annotation_dict` is a `PDFObjRef`
* https://github.com/Unstructured-IO/unstructured/issues/1921 where
`rect` is a `PDFObjRef`
### Test
Added three test files (both are larger than 500 KB) for unittests to
test:
* Repair entire doc
* Repair one page
* Reprocess failure after repairing one page (just return the elements
before error page in this case).
* Also seems like splitting the document into smaller pages could fix
this problem, but not sure why. For example, I saw error from reprocess
in the whole
[cancer.pdf](https://github.com/Unstructured-IO/unstructured/files/13461616/cancer.pdf)
doc, but no error when i split the pdf by error page....
* tested if i can repair the entire doc again in this case, saw other
error which means repairing is not helping imo
* PDFminer can process the whole doc after pikepdf only repaired the
entire doc in the first place, but we can't repair by pages in this way
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Summary:
Close: https://github.com/Unstructured-IO/unstructured/issues/1920
* stop passing in empty string from `languages` to tesseract, which will
result in passing empty string to language config `-l` for the tesseract
CLI
* also stop passing in duplicate language code from `languages` to
tesseract OCR
* if we failed to convert any iso languages from the `languages`
parameter, proceed OCR with `eng` as default
### Test
* First confirm the tesseract error `Estimating resolution as X` before
this:
* on the `unstructured-api` repo with main branch, run `make
run-web-app`
* curl to test error from empty string, or just any wrong input like `-F
'languages="eng,de"'`:
```
curl -X 'POST' 'http://0.0.0.0:8000/general/v0/general' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-F 'files=@sample-docs/layout-parser-paper-with-table.jpg' \
-F 'languages=""' \
-F 'strategy=hi_res' \
-F 'pdf_infer_table_structure=True' \
| jq -C . | less -R
```
* after this change:
* in your unstructured API env, cd to unstructured repo and install it locally with `pip install -e .`
* check out to this branch
* run `make run-web-app` again in api repo
* the curl command return output and see warning in log
---------
Co-authored-by: qued <64741807+qued@users.noreply.github.com>
Summary: Added support for AWS Bedrock embeddings. Leverages
"amazon.titan-tg1-large" for the embedding model.
Test
- find your aws secret access key and key id; make sure the account has
access to bedrock's tian embed model
- follow the instructions in
d5e797cd44/docs/source/bricks/embedding.rst (bedrockembeddingencoder)
---------
Co-authored-by: Ahmet Melek <39141206+ahmetmeleq@users.noreply.github.com>
Co-authored-by: Yao You <yao@unstructured.io>
Co-authored-by: Yao You <theyaoyou@gmail.com>
Co-authored-by: Ahmet Melek <ahmetmeleq@gmail.com>
### Description
As we add more and more steps to the pipeline (i.e. chunking, embedding,
table manipulation), it would help seperate the responsibility of each
of these into their own processes, running each in parallel using json
files to share data across. This will also help guarantee data is
serializable if this code was used in an actual pipeline. Following is a
flow diagram of the proposed changes. As part of this change:
* A parent pipeline class will be responsible for running each `node`,
which can optionally be run via multiprocessing if it supports it, or
not. Possible nodes at this moment:
* Doc factory: creates all the ingest docs via the source connector
* Source: reads/downloads all of the content to process to the local
filesystem to the location set by the `download_dir` parameter.
* Partition: runs partition on all of the downloaded content in json
format.
* Any number of reformat nodes that modify the partitioned content. This
can include chunking, embedding, etc.
* Write: push the final json into the destination via the destination
connector
* This pipeline relies on the information of the ingest docs to be
available via their serialization. An optimization was introduced with
the `IngestDocJsonMixin` which adds in all the `@property` fields to the
serialized json already being created via the `DataClassJsonMixin`
* For all intermediate steps (partitioning, reformatting), the content
is saved to a dedicated location on the local filesystem. Right now it's
set to `$HOME/.cache/unstructured/ingest/pipeline/STEP_NAME/`.
* Minor changes: made sense to move some of the config parameters
between the read and partition configs when I explicitly divided the
responsibility to download vs partition the content in the pipeline.
* The pipeline class only makes the doc factory, source and partition
nodes required, keeping with the logic that has been supported so far.
All reformatting nodes and write node are optional.
* Long term, there should also be some changes to the base configs
supported by the CLI to support pipeline specific configs, but for now
what exists was used to minimize changes in this PR.
* Final step to copy the final output to the location designated by the
`_output_filename` value of the ingest doc.
* Hashing occurs at each step by hashing the parameters of that step
(i.e. partition configs) along with the previous step via the filename
used. This allows each step to be the same _if_ all the parameters for
it have not changed and the content so far is the same.
* The only data that is shared and has writes to across processes is the
dictionary of ingest json data. This dict is created using the
`multiprocessing.manager.DictProxy` to make sure any interaction with it
is behind a lock.
### Minor refactors included:
* Utility methods added to extract configs from the click options
* Utility method to add common options to click commands.
* All writers moved to using the class approach which extracts a lot of
the common code so there's less copy-paste when new runners are added.
* Use `@property` for source metadata on base ingest doc to add logic to
call `update_source_metadata` if it's still `None` at the time it's
fetched.
### Additional bug fixes included
* Fsspec connectors were not serializable due to the `ingest_doc_cls`.
This was removed from the fields captured by the `@dataclass` decorator
and added in a `__post_init__` method.
* Various reddit connector params were missing. This doesn't have an
explicit ingest test at the moment so was never caught.
* Fsspec connector had the parent `update_source_metadata` misnamed as
`update_source_metadata_metadata` so it was never being called.
### Flow Diagram
### Description
This PR is two-fold:
**Embeddings:**
* Embeddings incorporated into the sharepoint source connector, which
will now call out to OpenAI and create embeddings if the flag is passed
in and the api key provided.
**Writing vector content (embeddings) to Azure cognitive search index:**
* The schema for the index expected to exist in Azure has been updated
to include the vector field type and a test script has been added to
test the new content being produced from the Sharepoint connector to
push the embedding content.
Some important notes about other changes in here:
* The embedding code had to be updated to patch the `to_dict` method on
elements to add `embeddings` to the dict output if that was added. While
the code originally added the embedding content, when `to_dict` was
called to save the content as json, this was lost.
Addresses
[#1332](https://github.com/Unstructured-IO/unstructured/issues/1332)
with `unstructured-inference` PR
[#208](https://github.com/Unstructured-IO/unstructured-inference/pull/208).
### Summary
- Add `image_path` to element metadata
- Pass parameters related to extracting images in PDF
- Preserve image elements ignored due to garbage text if
`el.metadata.image_path` is `True`
### Testing
from unstructured.partition.pdf import partition_pdf
f_path = "example-docs/embedded-images.pdf"
# default image output directory
elements = partition_pdf(
f_path,
strategy=strategy,
extract_images_in_pdf=True,
)
# specific image output directory
elements = partition_pdf(
f_path,
strategy=strategy,
extract_images_in_pdf=True,
image_output_dir_path=<directory path>,
)
If a layout model is used from unstructured-inference, you get back
class probabilities in the element metadata from partition.
extra-pdf-image-in in requirements already has the newest version of
unstructured-inference in there without a pinned version. Is there any
place else that the unstructured-inference version needs to be updated
to the required release version, 0.5.22?
* pip-compile in order to bump unstructured-inference
* Set the default `ocr_mode` back to `enitre_page` now that [this
error](https://github.com/Unstructured-IO/unstructured-inference/pull/183)
is addressed
* Explicitly add `sphinx-tabs` to `build.in`. This file provides
`docs/requirements.txt`.
* Remove a pinned `pydantic` version
* Fix a makefile command to `pip-compile` a missing ingest file.
Documentation Overhaul
- Added documentation hierarchy
- Added options for Bash vs Python for API & Upstream Connectors
- Added Introduction section (Overview, Key Concepts, Getting Started)
- Redid connectors section
- Installation is now broken up (needs further work)
* split dependencies by document type
* make pip-compile with new requirements
* add extra requirements to setup.py
* add in all docs; re pip-compile
* extra for all docs
* add pandas to xlsx
* dependency requires for tsv and csv
* handling for doc, docx and odt
* dependency check for pypandoc
* required dependencies for pandoc files
* xml and html
* markdown
* msg
* add in pdf
* add in pptx
* add in excel
* add lxml as base req
* extra all docs for local inference
* local inference installs all
* pin pillow version
* fixes for plain text tests
* fixes for doc
* update make commands
* changelog and version
* add xlrd
* update pip-compile
* pin numpy for python 3.8 support
* more constraints
* contraint on scipy
* update install docs
* constrain ipython
* add outlook to pip-compile
* more ipython constraints
* add extras to dockerfile
* pin office365 client
* few doc tweaks
* types as strings
* last pip-compile
* re pip-comple
* make tidy
* make tidy
* remove default strategy
* working on test
* fixed test, coordinates param needed to be included
* nits
* update changelog
* lint
* update requirements
* remove argilla; bump reqs
* enable py 3.11
* add 3.11 to setup.py
* make pip-compile
* ignore cli mypy errors
* install argilla
* fix constraints
* install argilla
* changelog and version
* skip argilla in docker
* dont import argilla in docker
* skip all of argilla if in container
* only import argilla if outside docker
* more docker skips
* remove weird pypi settings
tabulate is used by functions that extract tables from Microsoft documents, but there is nothing explicitly requiring the library. This was not caught by tests, because for some reason, tabulate is in base.txt.
This PR adds the dependency to base.in (which also puts it in setup.py), and recompiles the dependencies.
Addresses #631.
* Uses constraints to keep dependency versions more consistent.
* Moves all dependencies to .in files which are then ingested by setup.py.
* Adds script to check consistency of all extras.
* Adds consistency check to CI.
I should note that while it shouldn't be possible to cause a conflict between base.txt and any of the extras (because base.txt constrains all the extras) it is possible to get a conflict between two of the extras files. There are ways of trying to avoid that (like constraining each file by all the files that have already been processed before it in the order given in the make pip-compile target) but the ones I could think of seemed a little overwrought, and come with problems of their own. If a conflict arises, it should be flagged by CI or locally with make check-deps. When/if that happens, you can resolve the conflict by adding appropriate global constraints in requirements/constraints.txt.
Also note that if fileA.in is constrained by fileB.txt, then fileB.in should be compiled before fileA.in in the make pip-compile target. Otherwise fileA.in will be compiled with the old version of fileB.txt which can cause conflicts or keep dependencies from being updated properly.
