Bump unstructured from 0.10.4 to 0.10.19

[dependabot]

Bumps unstructured from 0.10.4 to 0.10.19.

Release notes

Sourced from unstructured's releases.

0.10.19

Enhancements

• Adds XLSX document level language detection Enhancing on top of language detection functionality in previous release, we now support language detection within .xlsx file type at Element level.
• bump unstructured-inference to 0.6.6 The updated version of unstructured-inference makes table extraction in hi_res mode configurable to fine tune table extraction performance; it also improves element detection by adding a deduplication post processing step in the hi_res partitioning of pdfs and images.
• Detect text in HTML Heading Tags as Titles 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, or narrative text, categorize it as a title.
• Update python-based docs Refactor docs to use the actual unstructured code rather than using the subprocess library to run the cli command itself.
• Adds data source properties to SharePoint, Outlook, Onedrive, Reddit, and Slack connectors These properties (date_created, date_modified, version, source_url, record_locator) are written to element metadata during ingest, mapping elements to information about the document source from which they derive. This functionality enables downstream applications to reveal source document applications, e.g. a link to a GDrive doc, Salesforce record, etc.
• Adds Table support for the add_chunking_strategy decorator to partition functions. In addition to combining elements under Title elements, user's can now specify the max_characters=<n> argument to chunk Table elements into TableChunk elements with text and text_as_html of length characters. This means partitioned Table results are ready for use in downstream applications without any post processing.
• Expose endpoint url for s3 connectors By allowing for the endpoint url to be explicitly overwritten, this allows for any non-AWS data providers supporting the s3 protocol to be supported (i.e. minio).
• change default hi_res model for pdf/image partition to yolox Now partitioning pdf/image using hi_res strategy utilizes yolox_quantized model isntead of detectron2_onnx model. This new default model has better recall for tables and produces more detailed categories for elements.
• XLSX can now reads subtables within one sheet Problem: Many .xlsx files are not created to be read as one full table per sheet. There are subtables, text and header along with more informations to extract from each sheet. Feature: This partition_xlsx now can reads subtable(s) within one .xlsx sheet, along with extracting other title and narrative texts. Importance: This enhance the power of .xlsx reading to not only one table per sheet, allowing user to capture more data tables from the file, if exists.
• Update Documentation on Element Types and Metadata: We have updated the documentation according to the latest element types and metadata. It includes the common and additional metadata provided by the Partitions and Connectors.

Fixes

• Fixes partition_pdf is_alnum reference bug Problem: The partition_pdf when attempt to get bounding box from element experienced a reference before assignment error when the first object is not text extractable. Fix: Switched to a flag when the condition is met. Importance: Crucial to be able to partition with pdf.
• Fix various cases of HTML text missing after partition Problem: Under certain circumstances, text immediately after some HTML tags will be misssing from partition result. Fix: Updated code to deal with these cases. Importance: This will ensure the correctness when partitioning HTML and Markdown documents.
• Fixes chunking when detection_class_prob appears in Element metadata Problem: when detection_class_prob appears in Element metadata, Elements will only be combined by chunk_by_title if they have the same detection_class_prob value (which is rare). This is unlikely a case we ever need to support and most often results in no chunking. Fix: detection_class_prob is included in the chunking list of metadata keys excluded for similarity comparison. Importance: This change allows chunk_by_title to operate as intended for documents which include detection_class_prob metadata in their Elements.

0.10.18

Enhancements

• Better detection of natural reading order in images and PDF's The elements returned by partition better reflect natural reading order in some cases, particularly in complicated multi-column layouts, leading to better chunking and retrieval for downstream applications. Achieved by improving the xy-cut sorting to preprocess bboxes, shrinking all bounding boxes by 90% along x and y axes (still centered around the same center point), which allows projection lines to be drawn where not possible before if layout bboxes overlapped.
• Improves partition_xml to be faster and more memory efficient when partitioning large XML files The new behavior is to partition iteratively to prevent loading the entire XML tree into memory at once in most use cases.
• Adds data source properties to SharePoint, Outlook, Onedrive, Reddit, Slack, and DeltaTable connectors These properties (date_created, date_modified, version, source_url, record_locator) are written to element metadata during ingest, mapping elements to information about the document source from which they derive. This functionality enables downstream applications to reveal source document applications, e.g. a link to a GDrive doc, Salesforce record, etc.
• Add functionality to save embedded images in PDF's separately as images This allows users to save embedded images in PDF's separately as images, given some directory path. The saved image path is written to the metadata for the Image element. Downstream applications may benefit by providing users with image links from relevant "hits."
• Azure Cognitive Search destination connector New Azure Cognitive Search destination connector added to ingest CLI. Users may now use unstructured-ingest to write partitioned data from over 20 data sources (so far) to an Azure Cognitive Search index.
• Improves salesforce partitioning Partitions Salesforce data as xlm instead of text for improved detail and flexibility. Partitions htmlbody instead of textbody for Salesforce emails. Importance: Allows all Salesforce fields to be ingested and gives Salesforce emails more detailed partitioning.
• Add document level language detection functionality. Introduces the "auto" default for the languages param, which then detects the languages present in the document using the langdetect package. Adds the document languages as ISO 639-3 codes to the element metadata. Implemented only for the partition_text function to start.
• PPTX partitioner refactored in preparation for enhancement. Behavior should be unchanged except that shapes enclosed in a group-shape are now included, as many levels deep as required (a group-shape can itself contain a group-shape).
• Embeddings support for the SharePoint SourceConnector via unstructured-ingest CLI The SharePoint connector can now optionally create embeddings from the elements it pulls out during partition and upload those embeddings to Azure Cognitive Search index.
• 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).
• Chunking support for the SharePoint SourceConnector via unstructured-ingest CLI The SharePoint connector can now optionally chunk the elements pulled out during partition via the chunking unstructured brick. This can be used as a stage before creating embeddings.

Features

• Adds links metadata in partition_pdf for fast strategy. Problem: PDF files contain rich information and hyperlink that Unstructured did not captured earlier. Feature: partition_pdf now can capture embedded links within the file along with its associated text and page number. Importance: Providing depth in extracted elements give user a better understanding and richer context of documents. This also enables user to map to other elements within the document if the hyperlink is refered internally.
• Adds the embedding module to be able to embed Elements Problem: Many NLP applications require the ability to represent parts of documents in a semantic way. Until now, Unstructured did not have text embedding ability within the core library. Feature: This embedding module is able to track embeddings related data with a class, embed a list of elements, and return an updated list of Elements with the embeddings property. The module is also able to embed query strings. Importance: Ability to embed documents or parts of documents will enable users to make use of these semantic representations in different NLP applications, such as search, retrieval, and retrieval augmented generation.

Fixes

• Fixes a metadata source serialization bug Problem: In unstructured elements, when loading an elements json file from the disk, the data_source attribute is assumed to be an instance of DataSourceMetadata and the code acts based on that. However the loader did not satisfy the assumption, and loaded it as a dict instead, causing an error. Fix: Added necessary code block to initialize a DataSourceMetadata object, also refactored DataSourceMetadata.from_dict() method to remove redundant code. Importance: Crucial to be able to load elements (which have data_source fields) from json files.
• Fixes issue where unstructured-inference was not getting updated Problem: unstructured-inference was not getting upgraded to the version to match unstructured release when doing a pip install. Solution: using pip install unstructured[all-docs] it will now upgrade both unstructured and unstructured-inference. Importance: This will ensure that the inference library is always in sync with the unstructured library, otherwise users will be using outdated libraries which will likely lead to unintended behavior.
• Fixes SharePoint connector failures if any document has an unsupported filetype Problem: Currently the entire connector ingest run fails if a single IngestDoc has an unsupported filetype. This is because a ValueError is raised in the IngestDoc's __post_init__. Fix: Adds a try/catch when the IngestConnector runs get_ingest_docs such that the error is logged but all processable documents->IngestDocs are still instantiated and returned. Importance: Allows users to ingest SharePoint content even when some files with unsupported filetypes exist there.

... (truncated)

Changelog

Sourced from unstructured's changelog.

0.10.19

Enhancements

• Adds XLSX document level language detection Enhancing on top of language detection functionality in previous release, we now support language detection within .xlsx file type at Element level.
• bump unstructured-inference to 0.6.6 The updated version of unstructured-inference makes table extraction in hi_res mode configurable to fine tune table extraction performance; it also improves element detection by adding a deduplication post processing step in the hi_res partitioning of pdfs and images.
• Detect text in HTML Heading Tags as Titles 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, or narrative text, categorize it as a title.
• Update python-based docs Refactor docs to use the actual unstructured code rather than using the subprocess library to run the cli command itself.
• Adds Table support for the add_chunking_strategy decorator to partition functions. In addition to combining elements under Title elements, user's can now specify the max_characters=<n> argument to chunk Table elements into TableChunk elements with text and text_as_html of length characters. This means partitioned Table results are ready for use in downstream applications without any post processing.
• Expose endpoint url for s3 connectors By allowing for the endpoint url to be explicitly overwritten, this allows for any non-AWS data providers supporting the s3 protocol to be supported (i.e. minio).
• change default hi_res model for pdf/image partition to yolox Now partitioning pdf/image using hi_res strategy utilizes yolox_quantized model isntead of detectron2_onnx model. This new default model has better recall for tables and produces more detailed categories for elements.
• XLSX can now reads subtables within one sheet Problem: Many .xlsx files are not created to be read as one full table per sheet. There are subtables, text and header along with more informations to extract from each sheet. Feature: This partition_xlsx now can reads subtable(s) within one .xlsx sheet, along with extracting other title and narrative texts. Importance: This enhance the power of .xlsx reading to not only one table per sheet, allowing user to capture more data tables from the file, if exists.
• Update Documentation on Element Types and Metadata: We have updated the documentation according to the latest element types and metadata. It includes the common and additional metadata provided by the Partitions and Connectors.

Fixes

• Fixes partition_pdf is_alnum reference bug Problem: The partition_pdf when attempt to get bounding box from element experienced a reference before assignment error when the first object is not text extractable. Fix: Switched to a flag when the condition is met. Importance: Crucial to be able to partition with pdf.
• Fix various cases of HTML text missing after partition Problem: Under certain circumstances, text immediately after some HTML tags will be misssing from partition result. Fix: Updated code to deal with these cases. Importance: This will ensure the correctness when partitioning HTML and Markdown documents.
• Fixes chunking when detection_class_prob appears in Element metadata Problem: when detection_class_prob appears in Element metadata, Elements will only be combined by chunk_by_title if they have the same detection_class_prob value (which is rare). This is unlikely a case we ever need to support and most often results in no chunking. Fix: detection_class_prob is included in the chunking list of metadata keys excluded for similarity comparison. Importance: This change allows chunk_by_title to operate as intended for documents which include detection_class_prob metadata in their Elements.

0.10.18

Enhancements

• Better detection of natural reading order in images and PDF's The elements returned by partition better reflect natural reading order in some cases, particularly in complicated multi-column layouts, leading to better chunking and retrieval for downstream applications. Achieved by improving the xy-cut sorting to preprocess bboxes, shrinking all bounding boxes by 90% along x and y axes (still centered around the same center point), which allows projection lines to be drawn where not possible before if layout bboxes overlapped.
• Improves partition_xml to be faster and more memory efficient when partitioning large XML files The new behavior is to partition iteratively to prevent loading the entire XML tree into memory at once in most use cases.
• Adds data source properties to SharePoint, Outlook, Onedrive, Reddit, Slack, DeltaTable connectors These properties (date_created, date_modified, version, source_url, record_locator) are written to element metadata during ingest, mapping elements to information about the document source from which they derive. This functionality enables downstream applications to reveal source document applications, e.g. a link to a GDrive doc, Salesforce record, etc.
• Add functionality to save embedded images in PDF's separately as images This allows users to save embedded images in PDF's separately as images, given some directory path. The saved image path is written to the metadata for the Image element. Downstream applications may benefit by providing users with image links from relevant "hits."
• Azure Cognite Search destination connector New Azure Cognitive Search destination connector added to ingest CLI. Users may now use unstructured-ingest to write partitioned data from over 20 data sources (so far) to an Azure Cognitive Search index.
• Improves salesforce partitioning Partitions Salesforce data as xlm instead of text for improved detail and flexibility. Partitions htmlbody instead of textbody for Salesforce emails. Importance: Allows all Salesforce fields to be ingested and gives Salesforce emails more detailed partitioning.
• Add document level language detection functionality. Introduces the "auto" default for the languages param, which then detects the languages present in the document using the langdetect package. Adds the document languages as ISO 639-3 codes to the element metadata. Implemented only for the partition_text function to start.
• PPTX partitioner refactored in preparation for enhancement. Behavior should be unchanged except that shapes enclosed in a group-shape are now included, as many levels deep as required (a group-shape can itself contain a group-shape).
• Embeddings support for the SharePoint SourceConnector via unstructured-ingest CLI The SharePoint connector can now optionally create embeddings from the elements it pulls out during partition and upload those embeddings to Azure Cognitive Search index.
• 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).
• Chunking support for the SharePoint SourceConnector via unstructured-ingest CLI The SharePoint connector can now optionally chunk the elements pulled out during partition via the chunking unstructured brick. This can be used as a stage before creating embeddings.

Features

• Adds links metadata in partition_pdf for fast strategy. Problem: PDF files contain rich information and hyperlink that Unstructured did not captured earlier. Feature: partition_pdf now can capture embedded links within the file along with its associated text and page number. Importance: Providing depth in extracted elements give user a better understanding and richer context of documents. This also enables user to map to other elements within the document if the hyperlink is refered internally.
• Adds the embedding module to be able to embed Elements Problem: Many NLP applications require the ability to represent parts of documents in a semantic way. Until now, Unstructured did not have text embedding ability within the core library. Feature: This embedding module is able to track embeddings related data with a class, embed a list of elements, and return an updated list of Elements with the embeddings property. The module is also able to embed query strings. Importance: Ability to embed documents or parts of documents will enable users to make use of these semantic representations in different NLP applications, such as search, retrieval, and retrieval augmented generation.

Fixes

• Fixes a metadata source serialization bug Problem: In unstructured elements, when loading an elements json file from the disk, the data_source attribute is assumed to be an instance of DataSourceMetadata and the code acts based on that. However the loader did not satisfy the assumption, and loaded it as a dict instead, causing an error. Fix: Added necessary code block to initialize a DataSourceMetadata object, also refactored DataSourceMetadata.from_dict() method to remove redundant code. Importance: Crucial to be able to load elements (which have data_source fields) from json files.
• Fixes issue where unstructured-inference was not getting updated Problem: unstructured-inference was not getting upgraded to the version to match unstructured release when doing a pip install. Solution: using pip install unstructured[all-docs] it will now upgrade both unstructured and unstructured-inference. Importance: This will ensure that the inference library is always in sync with the unstructured library, otherwise users will be using outdated libraries which will likely lead to unintended behavior.
• Fixes SharePoint connector failures if any document has an unsupported filetype Problem: Currently the entire connector ingest run fails if a single IngestDoc has an unsupported filetype. This is because a ValueError is raised in the IngestDoc's __post_init__. Fix: Adds a try/catch when the IngestConnector runs get_ingest_docs such that the error is logged but all processable documents->IngestDocs are still instantiated and returned. Importance: Allows users to ingest SharePoint content even when some files with unsupported filetypes exist there.
• Fixes Sharepoint connector server_path issue Problem: Server path for the Sharepoint Ingest Doc was incorrectly formatted, causing issues while fetching pages from the remote source. Fix: changes formatting of remote file path before instantiating SharepointIngestDocs and appends a '/' while fetching pages from the remote source. Importance: Allows users to fetch pages from Sharepoint Sites.

... (truncated)

Commits

• 8564d92 Update Metadata and Installation Documentation (#1646)
• 9960ce5 fix: chunking fails with detection_class_prob in metadata (#1637)
• 0a65fc2 feat: xlsx subtable extraction (#1585)
• 19d8bff feat: change default hi_res model to yolox quantized (#1607)
• 13453d6 Fix: Documentation for Unstructured API's (#1624)
• 8821689 Roman/s3 minio all cloud support (#1606)
• 1fb4642 chore: Table chunking (#1540)
• bcd0eee Feat: Detect all text in HTML Heading tags as titles (#1556)
• d6efd52 fix: isalnum referenced before assignment (#1586)
• b2e9976 roman/es ingest test fixes (#1610)
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

[dependabot]

Superseded by #9.