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'`
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.
Currently in the Elasticsearch Destination ingest test we are writing
the embeddings to a "float" type field. In order to leverage this field
for similarity search it should be mapped as "dense_vector" with the
respective dimensions assigned.
This PR updates that mapping and adds a test query to validate that this
works as expected.
This fixes the serialization of the Pinecone destination connector.
Presence of the PineconeIndex object breaks serialization due to
TypeError: cannot pickle '_thread.lock' object. This removes that object
before serialization.
The new "basic" chunking strategy and overlap options need to be
available from the ingest CLI. An ingest test of those features is also
welcome, both to verify the ingest feature and to defend against
regressions in the chunking code.
Add a local ingest test exercising both the "basic" chunking strategy
and intra-chunk overlap. Since there is no new source connector
involved, use the local ingest source and destination. Update
documentation to suit, filling in some details that hadn't made it into
the docs yet.
This PR updates Pinecone index creation in the ingest test due to a
recent update in Pinecone API.
Due to a change in Pinecone API, it is not allowed anymore to specify
both number of replicas and number of pods:
`Cannot specify both replicas and pods`
We solve it by removing the replica specification while sending the
index creation request.
```
Creating index ingest-test-28418
Index creation success: 201
```
Solution to issue
https://github.com/Unstructured-IO/unstructured/issues/2321.
simple_salesforce API allows for passing private key path or value. This
PR introduces this support for Ingest connector.
Salesforce parameter "private-key-file" has been renamed to
"private-key".
It can contain one of following:
- path to PEM encoded key file (as string)
- key contents (PEM encoded string)
If the provided value cannot be parsed as PEM encoded private key, then
the file existence is checked. This way private key contents are not
exposed to unnecessary underlying function calls.
FSSpec serialization caused conversion of JSON token to string with
single quotes. GCS requires JSON token in form of dict so this format is
now assured. Other forms of auth are not modified but there is improved
validation for all of the options.
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 a dictionary for helping map fully spelled out languages to
tesseract language codes
---------
Co-authored-by: Roman Isecke <136338424+rbiseck3@users.noreply.github.com>
### Description
* Make sure all destination connectors implement the base abstract
methods using the same signatures.
* Also leverage conform dict in the base methods to make sure it's
called in a consistent fashion.
* Additional updates to move the common code into the base destination
connector class
To test:
> cd docs && make HTML
changelogs:
- remove unindented line in destination connector's sql.rst file
- add elasticsearch page into destination_connector.rst file
This PR culminates the restructuring of chunking over my prior
dozen-or-so commits by adding the new options to the API and
documentation.
Separately I'll be adding a new ingest test to defend against
regression, although the integration test included in this PR will do a
pretty good job of that too.
### Description
The current approach injects the redacted text for all sensitive fields
regardless of if they have a value or not. This updates the code to only
replace the value with the redacted text if the value exists.
### Description
This PR handles two things:
* Fixes the serialization of the weaviate destination connector since
the client content breaks serialization when present due to `TypeError:
cannot pickle '_thread.lock' object`.
* Set finer auth control rather than generic dictionary on the CLI and
access config.
### Description
The session handler variable can be anything, because it's specific to
the SDK being used for the connector. This can break the serialization
depending on what that is. To avoid this all together, the session
handler itself is not serialized. Instead, it needs to be recreated if
an object is serialized and then deserialized.
MongoDB connector:
Issue:
[MongoDB
documentation](https://www.mongodb.com/docs/manual/reference/connection-string/)
states that characters `$ : / ? # [ ] @` must be percent encoded. URI
with password containing such special character will not be redacted.
Fix:
This fix removes usage of `unquote_plus` on password which allows
detected password to match with one inside URI and successfully replace
it.
Git connector:
Added very basic unit tests for repository filtering methods. Their
impact is rather minimal but showcases current limitation in
`is_file_type_supported` method.
The code makes edit to the `measure_text_extraction_accuracy` function
to allows dir of txt as well as json. The function also takes input
`output_type` to be either "json" or "txt" only, and checks if the files
under given directory/list contains only specified file type or not.
To test this feature, run the following code:
```PYTHONPATH=. python unstructured/ingest/evaluate.py measure-text-extraction-accuracy-command --output_dir <clean-text-path> --source_dir <cct-label-path> --output_type txt```
Reviewer: This PR probably reviews faster commit-by-commit. Each of the
commits is groomed and focuses on a separate clear aspect of this
implementation.
This PR adds inter-chunk overlap capability to chunking. It does not yet
expose it via the API.
Inter-chunk overlap is overlap between whole pre-chunks, prior to any
text-splitting required for oversized chunks. Contrast with intra-chunk
overlap implemented in the prior PR which implements overlap on these
latter text-splitting boundaries.
Inter-chunk overlap is disabled by default since a pre-chunk already has
a "clean" semantic boundary (composed of whole elements) and adding
overlap there introduces noise from the adjacent context. If the user
wants inter-chunk overlap they must specify `overlap_all=True` in the
options. Inter-chunk overlap uses the same `overlap` length value used
by intra-chunk overlap and does not overlap when that value is 0.
Fixes#2339
Fixes to HTML partitioning introduced with v0.11.0 removed the use of
`tabulate` for forming the HTML placed in `HTMLTable.text_as_html`. This
had several benefits, but part of `tabulate`'s behavior was to make
row-length (cell-count) uniform across the rows of the table.
Lacking this prior uniformity produced a downstream problem reported in
On closer inspection, the method used to "harvest" cell-text was
producing more text-nodes than there were cells and was sensitive to
where whitespace was used to format the HTML. It also "moved" text to
different columns in certain rows.
Refine the cell-text gathering mechanism to get exactly one text string
for each row cell, eliminating whitespace formatting nodes and producing
strict correspondence between the number of cells in the original HTML
table row and that placed in HTML.text_as_html.
HTML tables that are uniform (every row has the same number of cells)
will produce a uniform table in `.text_as_html`. Merged cells may still
produce a non-uniform table in `.text_as_html` (because the source table
is non-uniform).
- Adds a destination connector to upload processed output into a
PostgreSQL/Sqlite database instance.
- Users are responsible to provide their instances. This PR includes a
couple of configuration examples.
- Defines the scripts required to setup a PostgreSQL instance with the
unstructured elements schema.
- Validates postgres/pgvector embedding storage and retrieval
---------
Co-authored-by: potter-potter <david.potter@gmail.com>
Currently, we're using different kwarg names in partition() and
partition_pdf(), which has implications for the API since it goes
through partition().
### Summary
- rename `extract_element_types` -> `extract_image_block_types`
- rename `image_output_dir_path` to `extract_image_block_output_dir`
- rename `extract_to_payload` -> `extract_image_block_to_payload`
- rename `pdf_extract_images` -> `extract_images_in_pdf` in
`partition.auto`
- add unit tests to test element extraction for `pdf/image` via
`partition.auto`
### Testing
CI should pass.
To test:
> cd docs && make html
Sections:
- New User sign-up: (i) registration form, (ii) payment processing, and
(iii) use API key & URL
- API Account maintenance: (i) update billing, (ii) opt-in email, (iii)
rotate API key, and (iv) cancel plan
- Get Supports
Closes#2340
We need to make sure the custom url is passed to our client. The client
constructor takes the base url, so for compatibility we can continue to
take the full url and strip off the path.
To verify, run the api locally and confirm you can make calls to it.
```
# In unstructured-api
make run-web-app
# In ipython in this repo
from unstructured.partition.api import partition_via_api
filename = "example-docs/layout-parser-paper.pdf"
partition_via_api(filename=filename, api_url="http://localhost:8000")
```
Provide OCR tokens for table eval script. Right now
`unstructured-inference` can compute OCR components when they are not
passed in but in a future release we will be required to pass in OCR
results into table structure extraction model:
d3b2981313/CHANGELOG.md (0719)
This PR prepares for the upcoming change by passing ocr token into table
structure extraction process.
## test
Create a new virtual env that follows the setup in readme then upgrade
`inference` with `pip install unstructured-inference --upgrade`.
Run test `PYTHONPATH=. pytest
test_unstructured/metrics/test_table_structure.py` would fail on main
branch but fixed in this PR.
---------
Co-authored-by: Austin Walker <awalk89@gmail.com>
This PR intends to add [Qdrant](https://qdrant.tech/) as a supported
ingestion destination.
- Implements CLI and programmatic usage.
- Documentation update
- Integration test script
---
Clone of #2315 to run with CI secrets
---------
Co-authored-by: Anush008 <anushshetty90@gmail.com>
Co-authored-by: Roman Isecke <136338424+rbiseck3@users.noreply.github.com>
Closes#2323.
### Summary
- update logic to return "hi_res" if either `extract_images_in_pdf` or
`extract_element_types` is set
- refactor: remove unused `file` parameter from
`determine_pdf_or_image_strategy()`
### Testing
```
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(
filename="example-docs/embedded-images-tables.pdf",
extract_element_types=["Image"],
extract_to_payload=True,
)
image_elements = [el for el in elements if el.category == ElementType.IMAGE]
print(image_elements)
```
Closes#2302.
### Summary
- add functionality to get a Base64 encoded string from a PIL image
- store base64 encoded image data in two metadata fields: `image_base64`
and `image_mime_type`
- update the "image element filter" logic to keep all image elements in
the output if a user specifies image extraction
### Testing
```
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
extract_element_types=["Image", "Table"],
extract_to_payload=True,
)
```
or
```
from unstructured.partition.auto import partition
elements = partition(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
pdf_extract_element_types=["Image", "Table"],
pdf_extract_to_payload=True,
)
```
### Description
Leverage a similar pattern to what is used for connectors, where there
is a nested config dataclass as a field, along with cached content for
things like the client and sample embedding for each. This required an
update on the embeddings config in ingest and I left a TODO in there
because the current approach breaks on other encoders such as bedrock
because the parameters in that config don't map to all encoders. But
this keeps the existing functionality working.
This update makes sure all variables associated with the dataclass exist
when it's instantiated rather than being added in the `__post_init__()`
method or the `initialize()`, allowing other libraries like pydantic to
appropriately generate schemas from it. It also now follows the pattern
of the connectors in that each class has a nested config class used to
instantiate the client itself as well as a field/property approach used
to cache the client.
### Description
Convert all encoders to be based off dataclasses. Purpose: this will
allow encoders to be used in a generic way amongst other dataclasses.
Otherwise, it'll break validation in those parent dataclasses.
There are two distinct overlap operations with completely different
implementations. This is "intra-chunk" overlap, applying overlap to
chunks resulting from text-splitting an oversized element.
So if an oversized element had text "abcd efgh ijkl mnop qrst" and was
split at 15 chars with overlap of 5, it would produce "abcd efgh ijkl"
and "ijkl mnop qrst". Any inter-chunk overlap from the prior chunk and
applied at the beginning of the string (before "abcd") is handled in a
separate operation in the next PR.
Closes#2160
Explicitly adds `hi_res_model_name` as kwarg to relevant functions and
notes that `model_name` is to be deprecated.
Testing:
```
from unstructured.partition.auto import partition
filename = "example-docs/DA-1p.pdf"
elements = partition(filename, strategy="hi_res", hi_res_model_name="yolox")
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Co-authored-by: Steve Canny <stcanny@gmail.com>
Co-authored-by: Christine Straub <christinemstraub@gmail.com>
Co-authored-by: Yao You <yao@unstructured.io>
Co-authored-by: Yao You <theyaoyou@gmail.com>
