This change is adding to our `add_chunking_strategy` logic so that we
are able to chunk Table elements' `text` and `text_as_html` params. In
order to keep the functionality under the same `by_title` chunking
strategy we have renamed the `combine_under_n_chars` to
`max_characters`. It functions the same way for the combining elements
under Title's, as well as specifying a chunk size (in chars) for
TableChunk elements.
*renaming the variable to `max_characters` will also reflect the 'hard
max' we will implement for large elements in followup PRs
Additionally -> some lint changes snuck in when I ran `make tidy` hence
the minor changes in unrelated files :)
TODO:
✅ add unit tests
--> note: added where I could to unit tests! Some unit tests I just
clarified that the chunking strategy was now 'by_title' because we don't
have a file example that has Table elements to test the
'by_num_characters' chunking strategy
✅ update changelog
To manually test:
```
In [1]: filename="example-docs/example-10k.html"
In [2]: from unstructured.chunking.title import chunk_table_element
In [3]: from unstructured.partition.auto import partition
In [4]: elements = partition(filename)
# element at -2 happens to be a Table, and we'll get chunks of char size 4 here
In [5]: chunks = chunk_table_element(elements[-2], 4)
# examine text and text_as_html params
ln [6]: for c in chunks:
print(c.text)
print(c.metadata.text_as_html)
```
---------
Co-authored-by: Yao You <theyaoyou@gmail.com>
## Summary
This will increase the accuracy of hierarchies in HTML documents and
provide more accurate element categorization. If text is in an HTML
heading tag and is not a list item, address categorize it as a title.
## Testing
```
from unstructured.partition.html import partition_html
elements = partition_html(url="https://www.eda.gov/grants/2015")
```
Before, the date headers at the given url would not be correctly parsed
as titles, after this change they are now correctly identified.
A unit test to verify the functionality has been added:
`test_html_partition::test_html_heading_title_detection` that includes
values that were previously detected as narrative text and uncategorized
text
**Executive Summary**
Fix bug on the `get_word_bounding_box_from_element` function that
prevent `partition_pdf` to run.
**Technical Details**
- The function originally first define `isalnum` on the first index. Now
switched to conditional on flag value.
### Description
Updating the python version of the example docs to show how to run the
same code that the CLI runs, but using python. Rather than copying the
same command that would be run via the terminal and using the subprocess
library to run it, this updates it to use the supported code exposed in
the inference directory.
For now only the wikipedia one has been updated to get some opinions on
this before updating all other connector docs.
Would close out
https://github.com/Unstructured-IO/unstructured/issues/1445
Fix 4 cases of text missing after partition:
1. Text immediately after `<body>`
```html
<body>
missing1
<div>hello</div>
</body>
```
2. Text inside container and immediately after `<br/>`
```html
<div>hello<br/>missing2</div>
```
3. Text immediately after a text opening tag, if said tag contains
`<br/>`
```html
<p>missing3<br/>hello</p>
```
4. Text inside `<body>` if it is the only content (different cause from
case 1)
```html
<body>missing4</body>
```
Also fix problem causing
`test_unstructured/documents/test_html.py::test_exclude_tag_types` to
not work as intended.
This will close GitHub Issue#1543
The home directory for our dockerfile changed and broke this script. To
verify, try running the benchmark script:
```
export DOCKER_TEST=true
./scripts/performance/benchmark.sh
```
I'll pull in the latest changelog before merging.
- bump `unstructured-inference` to `0.6.6`
- specify default model name for element detection to be
`detectron2_onnx` to keep current behavior
- NOTE: the updated inference package by default would use yolox as
element detection model; this will be evaluated and enabled in a
separated PR
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
Occasionally the es test can fail because the index fail to be created
on the first try. Experiments show adding timeout doesn't help but add
retry mitigates the issue. See history of commits in branch:
yao/bump-inference-to-0.6.6
https://github.com/Unstructured-IO/unstructured/pull/1563
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
Fixes
docker exec unstructured-smoke-test /bin/bash -c
/home/notebook-user/test_unstructured_ingest/test-ingest-wikipedia.sh
/home/notebook-user/test_unstructured_ingest/test-ingest-wikipedia.sh:
line 10: python: command not found
in
https://github.com/Unstructured-IO/unstructured/blob/6ad4971/scripts/docker-smoke-test.sh#L43
that was preventing docker images from being built.
Closes GH Issue #1233.
### Summary
- add functionality to shrink all bounding boxes along x and y axes
(still centered around the same center point) before running xy-cut sort
### Evaluation
Run the followin gcommand for this
[PDF](https://utic-dev-tech-fixtures.s3.us-east-2.amazonaws.com/pastebin/patent-11723901-page2.pdf).
PYTHONPATH=. python examples/custom-layout-order/evaluate_xy_cut_sorting.py <file_path> <strategy>
- resolves an issue where occasionally deltalake writer results in
SIGABRT event though the writer finished writing table properly on linux
- this is first observed in ingest test
- Putting the writer into a process mitigates this problem by forcing
python to finish the deltalake rust backend to finish its tasks
## test
To test this it is best to setup an instance on a Linux system since the
problem has only been observed on Linux so far. Run
```bash
PYTHONPATH=. ./unstructured/ingest/main.py delta-table --num-processes 2 --metadata-exclude coordinates,filename,file_directory,metadata.data_source.date_processed,metadata.last_modified,metadata.date_created,metadata.detection_class_prob,metadata.parent_id,metadata.category_depth --table-uri ../tables/delta/ --preserve-downloads --verbose delta-table --write-column json_data --mode overwrite --table-uri file:///tmp/delta
```
Without this fix occasionally we'd encounter `SIGABTR`.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
This also follows what I have seen as the recommend way to define a file
package like this.
Also bumps minor versions from pip compile
Testing:
`pip install -e .`
Everything should build as normal
`❯ pip install -e .
Obtaining file:///Users/trevor/dev/unstructured
Installing build dependencies ... done
Checking if build backend supports build_editable ... done
Getting requirements to build editable ... done
Preparing editable metadata (pyproject.toml) ... done
Collecting scarf@ https://packages.unstructured.io/scarf.tgz (from
unstructured==0.10.17.dev16)
Using cached https://packages.unstructured.io/scarf.tgz (1.1 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done`
When new release goes out, I will test just plain pip install to verify
that functionality still works
Closes#1236. Partitions XML documents iteratively in most cases*, never
loading the entire tree into memory. This ends up being much faster.
(* The exception is when the argument `xml_path` is passed to filter
elements. I was not able to find a way in Python to compare XPaths while
streaming the elements, aside from writing a custom XPath parser. So the
shortest way forward was to bite the bullet and load the whole tree in
memory when filtering by XPath.)
Memory usage is about 20% of usage on `main` when processing a 470MB XML
file. Time to process is 10s vs 900s.
Output is slightly different, but appears to be an improvement, adding
lines of text that are skipped in current partitioning. No text is lost.
### Description
Optionally adds in chunking to the CLI which adds a flag to trigger
chunking and exposes the parameters used by the `chunk_by_title` method.
Runs chunking before the embedding step.
Opened to replace original PR
https://github.com/Unstructured-IO/unstructured/pull/1531
Fixes https://github.com/Unstructured-IO/unstructured-api/issues/237
The problem:
The `ElementMetadata` class was not able to ignore fields that it didn't
know about. This surfaced in `partition_via_api`, when the hosted api
schema is newer than the local `unstructured` version. In
`ElementMetadata.from_json()` we get errors such as `TypeError:
__init__() got an unexpected keyword argument 'parent_id'`.
The fix:
The `from_json` methods for these dataclasses should drop any unexpected
fields before calling `__init__`.
To verify:
This shouldn't throw an error
```
from unstructured.staging.base import elements_from_json
import json
test_api_result = json.dumps([
{
"type": "Title",
"element_id": "2f7cc75f6467bba468022c4c2875335e",
"metadata": {
"filename": "layout-parser-paper.pdf",
"filetype": "application/pdf",
"page_number": 1,
"new_field": "foo",
},
"text": "LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis"
}
])
elements = elements_from_json(text=test_api_result)
print(elements)
```
**Executive Summary**
Adds PDF functionality to capture hyperlink (external or internal) for
pdf fast strategy along with associate text.
**Technical Details**
- `pdfminer` associates `annotation` (links and uris) with bounding box
rather than text. Therefore, the link and text matching is not a perfect
pair but rather a logic-based and calculation matching from bounding box
overlapping.
- There is no word-level bounding box. Only character-level (access
using `LTChar`). Thus in order to get to word-level, there is a window
slicing through the text. The words are captured in alphanumeric and
non-alphanumeric separately, meaning it will split the word if contains
both, on the first encounter of non-alphanumeric.)
- The bounding box calculation is calculated using start and stop
coordinates for the corresponding word calculated from above. The
calculation is simply using distance between two dots.
The result now contains `links` in `metadata` as shown below:
```
"links": [
{
"text": "link",
"url": "https://github.com/Unstructured-IO/unstructured",
"start_index": 12
},
{
"text": "email",
"url": "mailto:unstructuredai@earlygrowth.com",
"start_index": 30
},
{
"text": "phone number",
"url": "tel:6505124019",
"start_index": 49
}
]
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: Klaijan <Klaijan@users.noreply.github.com>
Improves hierarchy from docx files by leveraging natural hierarchies
built into docx documents. Hierarchy can now be detected from an
indentation level for list bullets/numbers and by style name (e.g.
Heading 1, List Bullet 2, List Number).
Hierarchy detection is improved by determining category depth via the
following:
1. Check if the paragraph item has an indentation level (ilvl) xpath -
these are typically on list bullet/numbers. Return the indentation level
if it exists
2. Check the name of the paragraph style if it contains any category
depth information (e.g. Heading 1 vs Heading 2 or List Bullet vs List
Bullet 2). Return the category depth if found, else default to depth of
0.
3. Check the paragraph ilvl via the paragraph's style name. Outside of
the paragraph's metadata, docx stores default ilvls for various style
names, which requires a complex lookup. This check is yet to be
implemented, as the above methods cover most usecases but the
implementation is stubbed out.
---
Co-authored-by: Steve Canny <stcanny@gmail.com>
### 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.
This refactor solves a problem or two, the big one being recursing into
group-shapes to get all shapes on the slide, but mostly lays the
groundwork to allow us to refine further aspects such as list-item
detection, off-slide shape detection, and image-capture going forward.
### Summary
Uses `langdetect` to detect all languages present in the input document.
### Details
- Converts all language codes (whether user inputted or detected using
`langdetect`) to a standard ISO 639-3 code.
- Adds `languages` field to the metadata
- Will revisit how to nonstandardly represent simplified vs traditional
Chinese scripts internally (separate PR).
- Update ingest test results to add `languages` field to documents. Some
other side effects are changes in order of some elements and changes in
element categorization
### Test
You can test the detect_languages function individually by importing the
function and inputting a text sample and optionally a language:
```
text = "My lubimy mleko i chleb."
doc_langs = detect_languages(text)
print(doc_langs)
```
-> ['ces', 'pol', 'slk']
---------
Co-authored-by: Newel H <37004249+newelh@users.noreply.github.com>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: shreyanid <shreyanid@users.noreply.github.com>
Co-authored-by: Trevor Bossert <37596773+tabossert@users.noreply.github.com>
Co-authored-by: Ronny H <138828701+ron-unstructured@users.noreply.github.com>
This updates the docker image download url to pass through the scarf
gateway, this allows anonymous tracking of downloads
Related to:
https://github.com/Unstructured-IO/unstructured#chart_with_upwards_trend-analytics
Testing:
docker pull
downloads.unstructured.io/unstructured-io/unstructured:latest
Result:
Image should download
* Partitions Salesforce data as xlm instead of text for improved detail and flexibility
* Partitions htmlbody instead of textbody for Salesforce emails
### Description
New [Azure Cognitive
Search](https://azure.microsoft.com/en-us/products/ai-services/cognitive-search)
destination connector added. Writes each json element from the created
json files via partition and writes that content to an index.
**Bonus bug fix:** Due to a recent change where the default version of
python used in the repo was bumped to `3.10` from `3.8`, this means
running `pip-compile` now runs it against that version rather than the
lowest we support which is still `3.8`. This breaks the setup for those
lower versions because some of the versions pulled in by `pip-compile`
exist for `3.10` but not `3.8`. `pip-compile` was updates to run as a
script that checks the version of python being used first, which helps
guarantee that all dependencies meet the minimum python version
requirement.
Closes out https://github.com/Unstructured-IO/unstructured/issues/1466
@ron-unstructured reported that loading files with:
```
from unstructured.partition.pdf import partition_pdf
elements_yolox = partition_pdf(filename="1706.03762.pdf", strategy='hi_res', model_name="yolox")
print(elements_yolox)
```
Throws an error. After debugging the execution I found that the issue is
that an object of class Formula is being created, however, this class
doesn't contain an __init__ method. This PR solves the issue of adding a
constructor method with an empty string for the element.
The file can be found at:
https://drive.google.com/drive/folders/1hDumyps0hA4_d-GZxs3Hij15Cpa5fjWY?usp=sharing
After this PR is merged this file is correctly processed
We've created a custom domain, downloads.unstructured.io that redirects
to quay.io
(using https://scarf.sh/). This custom domain allows us to swap the
underlying container registry without impacting users. It also provides
us with important metrics about container and package usage, without
surfacing PII
like IP addresses.
Python package follows the same pattern at packages.unstructured.io
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>,
)
Closes https://github.com/Unstructured-IO/unstructured/issues/1319,
closes https://github.com/Unstructured-IO/unstructured/issues/1372
This module:
- implements EmbeddingEncoder classes which track embedding related data
- implements embed_documents method which receives a list of Elements,
obtains embeddings for the text within Elements, updates the Elements
with an attribute named embeddings , and returns the updated Elements
- the module uses langchain to obtain the embeddings
-----
- The PR additionally fixes a JSON de-serialization issue on the
metadata fields.
To test the changes, run `examples/embed/example.py`
