### Summary
Bumps to the latest `langchain-community` version to resolve
[CVE-2024-2965](https://nvd.nist.gov/vuln/detail/CVE-2024-2965).
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: MthwRobinson <MthwRobinson@users.noreply.github.com>
Moved numpy pin to `base.in` where it will be picked up by packaging.
Side note:
`constraints.txt` (formerly `constraints.in`) is a really useful
pattern: you put a constraint there, add that file as a `-c` requirement
in other files, and the constraint will be applied when pip-compiling
*only when needed* because the library is required by something else.
Neat! However, unfortunately, in my searches I've never found a similar
pattern for packaging, so any pins we want to propagate to user installs
need to be explicitly placed in the `.in` files.
So what is `constraints.txt` really doing for us? Well in the past I
think there have been instances where something is temporarily broken in
an upstream dependency but we expect it to be patched soon, but in the
meantime we want things to work in our CI builds and development
installs, so it's not worth pinning everywhere it's used. Having said
that, I'm coming to the conclusion that `constraints.txt` causes more
harm than good in the confusion it causes WRT packaging -- maybe we
should remove that pattern at some point.
## Summary
This PR addresses an issue where the code could attempt to run `soffice`
in multiple processes and closes#3284
The fix is to add a wait mechanism when there is another `soffice`
process running in already.
## Diagnosis of issue
- `soffice` can only have one process running when using the command
`soffice` as is.
- on main branch the function `partition.common.convert_office_doc`
simply spawns a subprocess to run `soffice` command to convert a `doc`
or `ppt` file into `docx` or `pptx` format.
- if there are multiple partition calls to process `doc` or `ppt` files
and they all want to spawn `soffice` subprocesses only one will succeed
while other processes will simply fail and return 1 from the subprocess
- in downstream this will lead to errors like `PackageNotFoundError:
Package not found at '/tmp/tmpac6lcu4w/document.docx'`
## solution
While there are
[ways](https://www.reddit.com/r/libreoffice/comments/agk3os/how_to_open_more_than_one_calc_instance_under/)
to circumvent the limit of `soffice` by setting a tmp file as user
installation env, these kind of solutions rely on the internals of
`soffice` and adds maintenance cost to track its changes.
This PR solves this problem by adding a wait mechanism:
- we first spawning a subprocess to run `soffice`
- if the `stdout` is empty and we still have wait time budget left the
function first checks if there is another `soffice` running
* If yes then the function waits for 0.01s before checking again;
* if no the functions spawns a subprocess to run `soffice` and return to
beginning of this step
* we need to return the the beginning to check if `stdout` is empty
because we could have another collision right after `soffice` becomes
available.
## test
This PR adds two unit tests.
Additionally this can be tested by running partition of `.doc` files
locally with multiprocessing.
### Summary
- bump unstructured-inference to `0.7.35` which fixed `ValueError` when
converting cells to HTML in the table processing subpipeline
- cut a release for `0.14.8`
---------
Co-authored-by: Matt Robinson <mrobinson@unstructured.io>
Co-authored-by: Matt Robinson <mrobinson@unstructuredai.io>
Thanks to @tullytim we have a new Kafka source and destination
connector. It also works with hosted Kafka via Confluent.
Documentation will be added to the Docs repo.
### Description
Choosing to use async needs to be very careful because if a connector is
set to use async, the pipeline will not fan out the inputs via
multiprocessing but instead it will be limited to run in a single
process under the assumption it has more benefit from async due to heavy
network traffic. This means the exact same code that is not optimized
for async and is blocking will force the pipeline to perform worse than
simply never marking the connector to use async since the pipeline will
fan that out using multiprocessing.
All connectors and processes in the pipeline we revisited to make sure
this criteria was met and updated accordingly:
* Currently the unstructured client does not support making requests
async, so this was moved over to use multiprocessing
* fsspec connector was updated to use the async client from the fsspec
library. This also required that the client be a `@property` fetched on
demand, otherwise the client would break the multiprocessing pool since
it maintains a thread lock and that can't be pickled when the fsspec
connector doesn't support async.
* elasticsearch was also updated to use the async client
* weaviate only recently came out with async support in their SDK at a
version that is higher than we can use in the open source repo, so a
TODO was left but otherwise moved to use multiprocessing
* all underlying embedders don't use async to embedder step must be
multiprocessing for now. TODO left to update underlying embedder classes
to optionally support async.
* Chunking parameters were not accurately being passed through from cli
to chunker params, this was fixed
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: rbiseck3 <rbiseck3@users.noreply.github.com>
### Summary
Version bumps for the week of 2024-06-17. There is a now a pin on
`numpy` due to a breaking change in the latest version that we'll need
to investigate and remove in a subsequent PR.
### Summary
- bump unstructured-inference to `0.7.35` which fixed syntax for
generated HTML tables
- update unit tests and ingest test fixtures to reflect changes in the
generated HTML tables
- cut a release for `0.14.6`
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
### Description
Add in tqdm support to show progress bar of status of each job when
being run. Supported for each mode (serial, async, multiprocess). Also
small timing wrapper around jobs to print out how long it took in total.
### Summary
Closes#3173. Removes the `overwrite_schema` kwarg from the Delta Table
connector and bumps the `deltalake` version. Per [this
PR](https://github.com/delta-io/delta-rs/pull/2554) in the `deltalake`
repo, the `overwrite_schema` kwarg is deprecated as of version `0.18.0`.
Users can specify `schema_mode="merge"` to obtain the same behavior.
- `schema_mode="merge"` is equivalent to `overwrite_schema=False`
- `schema_mode="overwrite"` is equivalent to `overwrite_schema=True`
Also adds an `engine` parameter that you can use to set `"rust"` or
`"pyarrow"` as the engine. `engine` defaults to `"pyarrow"` and
`schema_mode` defaults to `None`, which is consistent with the behavior
in `deltalake` documented
[here](https://delta-io.github.io/delta-rs/api/delta_writer/).
### Testing
The Delta Table ingest tests should pass on this PR.
---------
Co-authored-by: Ahmet Melek <39141206+ahmetmeleq@users.noreply.github.com>
**Summary**
`partition_msg()` previously used the `msg_parser` library for parsing
Outlook MSG email files (.msg files). The `msg_parser` library is
unmaintained and has several major shortcomings such as not being able
to parse MSG files with 8-bit encoded strings and not reliably
extracting attachments.
Use the new and permissively licenced `python-oxmsg` library instead.
**Additional Context**
For reviewability purposes, this PR temporarily places the new
`partition_msg()` implementation in `new_msg.py` and references that
implementation from `msg.py`. `new_msg.py` will be renamed to `msg.py`
in a closely following PR. This avoids a very messy interleaving of
hunks in a diff between the old and re-written `partition_msg()`
implementation.
Fixes#2481Fixes#3006
Since we incorporate a newer feature from `python-docx`
[here](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/partition/docx.py#L521),
we should make the version of `python-docx` that first supports that
method an explicit requirement.
I didn't pip recompile since our generated dependencies already have
`python-docx==1.1.2`, but I can do that if someone thinks it's
necessary.
Original PR was #3069. Merged in to a feature branch to fix dependency
and linting issues. Application code changes from the original PR were
already reviewed and approved.
------------
Original PR description:
Adding VoyageAI embeddings
Voyage AI’s embedding models and rerankers are state-of-the-art in
retrieval accuracy.
---------
Co-authored-by: fzowl <160063452+fzowl@users.noreply.github.com>
Co-authored-by: Liuhong99 <39693953+Liuhong99@users.noreply.github.com>
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.
This pull request allows to return predictions in raw cell
representation from table transformer. It will be later used to save
prediction in a cells format for simpler metrics calculation.
This PR has to be merged, after
https://github.com/Unstructured-IO/unstructured-inference/pull/335
This PR adds a third OCR provider, alongside Tesseract and Paddle: the
[Google Cloud Vision API](https://cloud.google.com/vision).
It can be used similarly to other OCR methods: set the `OCR_AGENT`
environment variable to the path to the OCR module
(`unstructured.partition.utils.ocr_models.google_vision_ocr.OCRAgentGoogleVision`).
You also need to set the credentials to use Google APIs, for instance by
setting the `GOOGLE_APPLICATION_CREDENTIALS` environment variable.
---------
Co-authored-by: christinestraub <christinemstraub@gmail.com>
**Summary**
Update dependencies to use the new version of `unstructured-inference`
released yesterday. Remedy a few small problems with `make pip-compile`
that stood in the way.
### 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
This PR is the second part of fixing "embedded text not getting merged
with inferred elements", the first part is done in
https://github.com/Unstructured-IO/unstructured-inference/pull/331.
### Summary
- replace `Rectangle.is_in()` with `Rectangle.is_almost_subregion_of()`
when removing pdfminer (embedded) elements that were merged with
inferred elements
- use env_config `EMBEDDED_TEXT_AGGREGATION_SUBREGION_THRESHOLD`
introduced in the [first
part](https://github.com/Unstructured-IO/unstructured-inference/pull/331)
when removing pdfminer (embedded) elements that were merged with
inferred elements
- bump `unstructured-inference` to 0.7.25
### Testing
PDF:
[pwc-financial-statements-p114.pdf](https://github.com/Unstructured-IO/unstructured/files/14707146/pwc-financial-statements-p114.pdf)
```
$ pip uninstall unstructured-inference -y
$ git clone -b fix/embedded-text-not-getting-merged-with-inferred-elements git@github.com:Unstructured-IO/unstructured-inference.git && cd unstructured-inference
$ pip install -e .
```
```
elements = partition_pdf(
filename="pwc-financial-statements-p114.pdf",
strategy="hi_res",
infer_table_structure=True,
extract_image_block_types=["Image"],
)
table_elements = [el for el in elements if el.category == "Table"]
print(table_elements[0].text)
```
---------
Co-authored-by: Antonio Jose Jimeno Yepes <antonio.jimeno@gmail.com>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
### Description
This PR resolved the following open issue:
[bug/bedrock-encoder-not-supported-in-ingest](https://github.com/Unstructured-IO/unstructured/issues/2319).
To do so, the following changes were made:
* All aws configs were added as input parameters to the CLI
* These were mapped to the bedrock embedder when an embedder is
generated via `get_embedder`
* An ingest test was added to call the aws bedrock service
* Requirements for boto were bumped because the first version to
introduce the bedrock runtime, which is required to hit the bedrock
service, was introduced in version `1.34.63`, which was ahead of the
version of boto pinned.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: rbiseck3 <rbiseck3@users.noreply.github.com>
Thanks to @mogith-pn from Clarifai we have a new destination connector!
This PR intends to add Clarifai as a ingest destination connector.
Access via CLI and programmatic.
Documentation and Examples.
Integration test script.
Fixes Onedrive bug the same way Ryan fixed the Sharepoint error. (both
are microsoft products)
https://github.com/Unstructured-IO/unstructured/pull/2591https://github.com/Unstructured-IO/unstructured/pull/2592/files
We are seeing occurrences of inconsistency in the timestamps returned by
Onedrive when fetching created and modified dates. Furthermore, in
future versions of this library, a datetime object will be returned
rather than a string.
Changes
This adds logic to guarantee Onedrive dates will be properly formatted
as ISO, regardless of the format provided by the onedrive library.
Bumps timestamp format output to include timezone offset (as we do with
others)
Adds unit tests for isofomat.
json_to_dict already unit tested here:
https://github.com/Unstructured-IO/unstructured/blob/main/test_unstructured_ingest/unit/test_utils.py
Adds small change for AstraDB to allow them to see what source called
their api
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>
Thanks to Eric Hare @erichare at DataStax we have a new destination
connector.
This Pull Request implements an integration with [Astra
DB](https://datastax.com) which allows for the Astra DB Vector Database
to be compatible with Unstructured's set of integrations.
To create your Astra account and authenticate with your
`ASTRA_DB_APPLICATION_TOKEN`, and `ASTRA_DB_API_ENDPOINT`, follow these
steps:
1. Create an account at https://astra.datastax.com
2. Login and create a new database
3. From the database page, in the right hand panel, you will find your
API Endpoint
4. Beneath that, you can create a Token to be used
Some notes about Astra DB:
- Astra DB is a Vector Database which allows for high-performance
database transactions, and enables modern GenAI apps [See
here](https://docs.datastax.com/en/astra/astra-db-vector/get-started/concepts.html)
- It supports similarity search via a number of methods [See
here](https://docs.datastax.com/en/astra/astra-db-vector/get-started/concepts.html#metrics)
- It also supports non-vector tables / collections
The current `test-ingest-src.sh` and `evaluation-metrics` do not allow
passing the `EXPORT_DIR` (`OUTPUT_ROOT` in `evaluation-metrics`). It is
currently saving at the current working directory
(`unstructured/test_unstructured_ingest`). When running the eval from
`core-product`, all outputs is now saved at
`core-product/upstream-unstructured/test_unstructured_ingest` which is
undesirable.
This PR modifies two scripts to accommodate such behavior:
1. `test-ingest-src.sh` - assign `EVAL_OUTPUT_ROOT` to the value set
within the environment if exist, or the current working directory if
not. Then calls to run `evaluation-metrics.sh`.
2. `evaluation-metrics.sh` - accepting param from `test-ingest-src.sh`
if exist, or to the value set within the environment if exist, or the
current directory if not.
(Note: I also add param to `evaluation-metrics.sh` because it makes
sense to allow a separate run to be able to specify an export directory)
This PR should work in sync with another PR under `core-product`, which
I will add the link here later.
**To test:**
Run the script below, change `$SCRIPT_DIR` as needed to see the result.
```
export OVERWRITE_FIXTURES=true
./upstream-unstructured/test_unstructured_ingest/src/s3.sh
SCRIPT_DIR=$(dirname "$(realpath "$0")")
bash -x ./upstream-unstructured/test_unstructured_ingest/evaluation-metrics.sh text-extraction "$SCRIPT_DIR"
```
----
This PR also updates the requirements by `make pip-compile` since the
`click` module was not found.
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
Removed `pillow` pin and recompiled. I think it was originally there to
address a conflict, which, as far as I can tell, no longer exists. Also
a security vulnerability was discovered in the older version of
`pillow`.
#### Testing:
CI should pass.
Update `black` and apply changes to affected files. I separated this PR
so we can have a look at the changes and decide whether we want to:
1. Go forward with the new formatting
2. Change the black config to make the old formatting valid
3. Get rid of black entirely and just use `ruff`
4. Do something I haven't thought of
.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>
When a partitioned or embedded document json has null values, those get
converted to a dictionary with None values.
This happens in the metadata. I have not see it in other keys.
Chroma and Pinecone do not like those None values.
`flatten_dict` has been modified with a `remove_none` arg to remove keys
with None values.
Also, Pinecone has been pinned at 2.2.4 because at 3.0 and above it
breaks our code.
---------
Co-authored-by: potter-potter <david.potter@gmail.com>
### Description
This adds in a destination connector to write content to the Databricks
Unity Catalog Volumes service. Currently there is an internal account
that can be used for testing manually but there is not dedicated account
to use for testing so this is not being added to the automated ingest
tests that get run in the CI.
To test locally:
```shell
#!/usr/bin/env bash
path="testpath/$(uuidgen)"
PYTHONPATH=. python ./unstructured/ingest/main.py local \
--num-processes 4 \
--output-dir azure-test \
--strategy fast \
--verbose \
--input-path example-docs/fake-memo.pdf \
--recursive \
databricks-volumes \
--catalog "utic-dev-tech-fixtures" \
--volume "small-pdf-set" \
--volume-path "$path" \
--username "$DATABRICKS_USERNAME" \
--password "$DATABRICKS_PASSWORD" \
--host "$DATABRICKS_HOST"
```
Adds OpenSearch as a source and destination.
Since OpenSearch is a fork of Elasticsearch, these connectors rely
heavily on inheriting the Elasticsearch connectors whenever possible.
- Adds OpenSearch source connector to be able to ingest documents from
OpenSearch.
- Adds OpenSearch destination connector to be able to ingest documents
from any supported source, embed them and write the embeddings /
documents into OpenSearch.
- Defines an example unstructured elements schema for users to be able
to setup their unstructured OpenSearch indexes easily.
---------
Co-authored-by: potter-potter <david.potter@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'`
