The purpose of this PR is to refactor OCR-related modules to reduce
unnecessary module imports to avoid potential issues (most likely due to
a "circular import").
### Summary
- add `inference_utils` module
(unstructured/partition/pdf_image/inference_utils.py) to define
unstructured-inference library related utility functions, which will
reduce importing unstructured-inference library functions in other files
- add `conftest.py` in `test_unstructured/partition/pdf_image/`
directory to define fixtures that are available to all tests in the same
directory and its subdirectories
### Testing
CI should pass
I accidentally added Vectara to setup and makefile. But there are no
dependencies for Vectara
This removes Vectara from those files.
---------
Co-authored-by: potter-potter <david.potter@gmail.com>
Thanks to Ofer at Vectara, we now have a Vectara destination connector.
- There are no dependencies since it is all REST calls to API
-
---------
Co-authored-by: potter-potter <david.potter@gmail.com>
This is nice to natively support both Tesseract and Paddle. However, one
might already use another OCR and might want to keep using it (for
quality reasons, for cost reasons etc...).
This PR adds the ability for the user to specify its own OCR agent
implementation that is then called by unstructured.
I am new to unstructured so don't hesitate to let me know if you would
prefer this being done differently and I will rework the PR.
---------
Co-authored-by: Yao You <theyaoyou@gmail.com>
Co-authored-by: Yao You <yao@unstructured.io>
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>
This PR is the last in a series of PRs for refactoring and fixing the
language parameters (`languages` and `ocr_languages` so we can address
incorrect input by users. See #2293
It is recommended to go though this PR commit-by-commit and note the
commit message. The most significant commit is "update
check_languages..."
- there are multiple places setting the default `hi_res_model_name` in
both `unstructured` and `unstructured-inference`
- they lead to inconsistency and unexpected behaviors
- this fix removes a helper in `unstructured` that tries to set the
default hi_res layout detection model; instead we rely on the
`unstructured-inference` to provide that default when no explicit model
name is passed in
## test
```bash
UNSTRUCTURED_INCLUDE_DEBUG_METADATA=true ipython
```
```python
from unstructured.partition.auto import partition
# find a pdf file
elements = partition("foo.pdf", strategy="hi_res")
assert elements[0].metadata.detection_origin == "yolox"
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
Formatting of link_texts was breaking metadata storage. Turns out it
didn't need any conforming and came in correctly from json.
---------
Co-authored-by: potter-potter <david.potter@gmail.com>
To test:
> cd docs && make html
Change logs:
* Updates the best practice for table extraction to use
`skip_infer_table_types` instead of `pdf_infer_table_structure`.
* Fixed CSS issue with a duplicate search box.
* Fixed RST warning message
* Fixed typo on the Intro page.
We have added a new version of chipper (Chipperv3), which needs to allow
unstructured to effective work with all the current Chipper versions.
This implies resizing images with the appropriate resolution and make
sure that Chipper elements are not sorted by unstructured.
In addition, it seems that PDFMiner is being called when calling
Chipper, which adds repeated elements from Chipper and PDFMiner.
To evaluate this PR, you can test the code below with the attached PDF.
The code writes a JSON file with the generated elements. The output can
be examined with `cat out.un.json | python -m json.tool`. There are
three things to check:
1. The size of the image passed to Chipper, which can be identiied in
the layout_height and layout_width attributes, which should have values
3301 and 2550 as shown in the example below:
```
[
{
"element_id": "c0493a7872f227e4172c4192c5f48a06",
"metadata": {
"coordinates": {
"layout_height": 3301,
"layout_width": 2550,
```
2. There should be no repeated elements.
3. Order should be closer to reading order.
The script to run Chipper from unstructured is:
```
from unstructured import __version__
print(__version__.__version__)
import json
from unstructured.partition.auto import partition
from unstructured.staging.base import elements_to_json
elements = json.loads(elements_to_json(partition("Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper-p6.pdf", strategy="hi_res", model_name="chipperv3")))
with open('out.un.json', 'w') as w:
json.dump(elements, w)
```
[Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper-p6.pdf](https://github.com/Unstructured-IO/unstructured/files/13817273/Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper-p6.pdf)
---------
Co-authored-by: Antonio Jimeno Yepes <antonio@unstructured.io>
### Summary
Closes#2412. Adds support for YAML MIME types and treats them as plain
text. In response to `500` errors that the API currently returns if the
MIME type is `text/yaml`.
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>
### Summary
Adds a driver with `unstructured` version information to the MongoDB
driver.
### Testing,
Good to go as long as the MongoDB ingest test run successfully.
setup.py is currently pointing to the wrong location for the
databricks-volumes extra requirements. This PR updates to point to the
correct location.
## Testing
Tested by installing from local source with `pip install .`
### 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"
```
FSSpec destination connectors did not use `check_connection`. There was
an error when trying to `ls` destination directory - it may not exist at
the moment of creation of connector.
Now `check_connection` calls `ls` on bucket root and this method is
called on `initialize` of destination connector.
To test:
> cd docs && make html
Changelogs:
* Fixed sphinx error due to malformed rst table on partition page
* Updated API Params, ie. `extract_image_block_types` and
`extract_image_block_to_payload`
* Updated image filetype supports
This PR is one in a series of PRs for refactoring and fixing the
languages parameter so it can address incorrect input by users. #2293
This PR adds _clean_ocr_languages_arg. There are no calls to this
function yet, but it will be called in later PRs related to this series.
Connector data source versions should always be string values, however
we were using the integer checksum value for the version for fsspec
connectors. This casts that value to a string.
## Changes
* Cast the checksum value to a string when assigning the version value
for fsspec connectors.
* Adds test to validate that these connectors will assign a string value
when an integer checksum is fetched.
## Testing
Unit test added.
Closes#2320 .
### Summary
In certain circumstances, adjusting the image block crop padding can
improve image block extraction by preventing extracted image blocks from
being clipped.
### Testing
- PDF:
[LM339-D_2-2.pdf](https://github.com/Unstructured-IO/unstructured/files/13968952/LM339-D_2-2.pdf)
- Set two environment variables
`EXTRACT_IMAGE_BLOCK_CROP_HORIZONTAL_PAD` and
`EXTRACT_IMAGE_BLOCK_CROP_VERTICAL_PAD`
(e.g. `EXTRACT_IMAGE_BLOCK_CROP_HORIZONTAL_PAD = 40`,
`EXTRACT_IMAGE_BLOCK_CROP_VERTICAL_PAD = 20`
```
elements = partition_pdf(
filename="LM339-D_2-2.pdf",
extract_image_block_types=["image"],
)
```
This fixes the serialization of the ChromaDB destination connector.
Presence of the _collection object breaks serialization due to
TypeError: cannot pickle 'module' object. This removes that object
before serialization.
This PR updates flatten_dict function to support flattening tuples.
This is necessary for objects like Coordinates, when the object is not
written to the disk, therefore not being converted to a list before
getting flattened.
This refactor removes `_convert_to_standard_langcode` and replaces it
with calling `_get_iso639_language_object` with a string slice.
Use of TESSERACT_LANGUAGES_AND_CODES, which was added to
`_convert_to_standard_langcode` previously, is moved to the relevant
part where `_convert_to_standard_langcode` was previously called.
If/else statements replace the list comprehension for readability and
`langdetect_langs.append("zho")` replaces
`_convert_to_standard_langcode("zh")` since that always returned
`"zho"`.
Propagating the openssl revert made in the base image:
https://github.com/Unstructured-IO/base-images/pull/13
Note that I messed up and wrote over the existing 9.2-9 image. Any
current prs will need to rebase in order to get a working dockerfile.
To test:
> cd docs && make html
> click "Ask AI" button on the bottom right-hand corner
Changelogs:
* Installed kapa.ai widget
* fixed sphinx errors in opensearch & elasticsearch documentation
### Summary
Adds support for bitmap images (`.bmp`) in both file detection and
partitioning. Bitmap images will be processed with `partition_image`
just like JPGs and PNGs.
### Testing
```python
from unstructured.file_utils.filetype import detect_filetype
from unstructured.partition.auto import partition
from PIL import Image
filename = "example-docs/layout-parser-paper-with-table.jpg"
bmp_filename = "~/tmp/ayout-parser-paper-with-table.bmp"
img = Image.open(filename)
img.save(bmp_filename)
detect_filetype(filename=bmp_filename) # Should be FileType.BMP
elements = partition(filename=bmp_filename)
```
To test:
> cd docs && make html
Changelogs:
* Added verbiage about the cap limit and data usage for the Freemium AP
* Added deprecated warning on Staging bricks
* Added warning and code examples to use the SaaS API Endpoints using
CLI-vs-SDKs
* Fixed example page formatting
* Added deprecation warning on ``model_name`` param in favor of
``hi_res_model_name``
* Added ``extract_images_in_pdf`` usage and code example in
``partition_pdf`` section
* Reorganized and improved the documentation Intro section
This fixes the serialization of the Elasticsearch destination connector.
Presence of the _client object breaks serialization due to TypeError:
cannot pickle '_thread.lock' object. This removes that object before
serialization.
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>
To test:
cd docs && make HTML
changelogs:
point main readme to the correct connector html page
point chroma docs to correct sample code
---------
Co-authored-by: potter-potter <david.potter@gmail.com>
There are several public interface points for chunking and they all
provide a default for arguments like `max_charactes`. These defaults are
provided by literal values. Keeping these synchronized has become a
problem.
Declare constant values for chunking argument default values and use
those wherever a non-trivial default is used in an end-user facing API
function.
This PR is one in a series of PRs for refactoring and fixing the
`languages` parameter so it can address incorrect input by users. #2293
Refactor `_convert_language_code_to_pytesseract_lang_code` and extract
`_get_iso639_language_object` to its own function
```
from unstructured.partition.lang import _convert_language_code_to_pytesseract_lang_code as convert
convert("English") # this will raise an error on both main and this branch
convert("en") # this will return "eng" on both branches
```
Connectors use predictable result file naming convention so consumers of
library can write code in abstraction of particular connector.
This change introduces compatibility with said naming convention.
`_output_filename` returns now filename with format.
### Summary
The goal of this PR is to keep all image elements when using "hi_res"
strategy. Previously, `Image` elements with small chunks of text were
ignored unless the image block extraction parameters
(`extract_images_in_pdf` or `extract_image_block_types`) were specified.
Now, all image elements are kept regardless of whether the image block
extraction parameters are specified.
### Testing
- on `main` branch,
```
elements = partition_pdf(
filename="example-docs/embedded-images.pdf",
strategy="hi_res",
)
image_elements = [el for el in elements if el.category == ElementType.IMAGE]
print("number of image elements: ", len(image_elements))
```
The above code will display `number of image elements: 0`.
- on this `feature` branch,
The same code will display `number of image elements: 3`
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.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'`
