## **Summary**
By adding hierarchy to unstructured elements, users will have more
information for implementing vector db/LLM chunking strategies. For
example, text elements could be queried by their preceding title
element. The hierarchy is implemented by a parent_id tag in the
element's metadata.
### Features
- Introduces a parent_id to ElementMetadata (The id of the parent
element, not a pointer)
- Creates a rule set for assigning hierarchies. Sensible default is
assigned, with an optional override parameter
- Sets element parent ids if there isn't an existing parent id or
matches the ruleset
### How it works
Hierarchies are assigned via a parent id field in element metadata.
Elements are read sequentially and evaluated against a ruleset. For
example take the following elements:
1. Title, "This is the Title"
2. Text, "this is the text"
And the ruleset: `{"title": ["text"]}`. When evaluated, the parent_id of
2 will be the id of 1. The algorithm for determining this is more
complex and resolves several edge cases, so please read the code for
further details.
### Schema Changes
```
@dataclass
class ElementMetadata:
coordinates: Optional[CoordinatesMetadata] = None
data_source: Optional[DataSourceMetadata] = None
filename: Optional[str] = None
file_directory: Optional[str] = None
last_modified: Optional[str] = None
filetype: Optional[str] = None
attached_to_filename: Optional[str] = None
+ parent_id: Optional[Union[str, uuid.UUID, NoID, UUID]] = None
+ category_depth: Optional[int] = None
...
```
### Testing
```
from unstructured.partition.auto import partition
from typing import List
elements = partition(filename="./unstructured/example-docs/fake-html.html", strategy="auto")
for element in elements:
print(
f"Category: {getattr(element, 'category', '')}\n"\
f"Text: {getattr(element, 'text', '')}\n"
f"ID: {element.id}\n" \
f"Parent ID: {element.metadata.parent_id}\n"\
f"Depth: {element.metadata.category_depth}\n" \
)
```
### Additional Notes
Implementing this feature revealed a possibly undesired side-effect in
how element metadata are processed. In
`unstructured/partition/common.py` the `_add_element_metadata` is
invoked as part of the `add_metadata_with_filetype` decorator for
filetype partitioning. This method is intended to add additional
information to the metadata generated with the element including
filename and filetype, however the existing metadata is merged into a
newly created metadata object rather than the other way around. Because
of the way it's structured, new metadata fields can easily be forgotten
and pose debugging challenges to developers. This likely warrants a new
issue.
I'm guessing that the implementation is done this way to avoid issues
with deserializing elements, but could be wrong.
---------
Co-authored-by: Benjamin Torres <benjats07@users.noreply.github.com>
### Summary
Partial solution to #1185.
Related to #1222.
Creates decorator from `chunk_by_title` cleaning brick.
Breaks a document into sections based on the presence of Title elements.
Also starts a new section under the following conditions:
- If metadata changes, indicating a change in section or page or a
switch to processing attachments. If `multipage_sections=True`, sections
can span pages. `multipage_sections` defaults to True.
- If the length of the section exceeds `new_after_n_chars` characters.
The default is 1500. The **chunking function does not split individual
elements**, so it's possible for a section to exceed that threshold if
an individual element if over `new_after_n_chars characters`, which
could occur with a long NarrativeText element.
Combines sections under these conditions
- Sections under `combine_under_n_chars` characters are combined. The
default is 500.
### Testing
from unstructured.partition.html import partition_html
url = "https://understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-27-2023-0"
chunks = partition_html(url=url, chunking_strategy="by_title")
for chunk in chunks:
print(chunk)
print("\n\n" + "-"*80)
input()
Update `test_json` to not use auto partition due to dependencies. Previously, to run `test_json` requires full requirements installation library to read file types, including but not limited to, docx, pptx, as well as others. Therefore the test will raise error with base installation. With the update, this fix also add to other test files to check its invariant with `elements_to_json`.
### Summary
Closes#1018. Enables `partition_email` and `partition_msg` to detect if
an email has PGP encrypted content. Based on the specification in [RFC
2015](https://www.ietf.org/rfc/rfc2015.txt). The test emails are based
on the example email in the spec. If PGP detected content is detected, a
warning is emitted and an empty set of lists is returned.
### Testing
```python
from unstructured.partition_email import partition_email
filename = "example-docs/eml/fake-encrypted.eml"
partition_email(filename=filename)
```
```python
from unstructured.partition_msg import partition_msg
filename = "example-docs/fake-encrypted.msg"
partition_msgl(filename=filename)
```
**Summary**
Closes#747
* Create CI Pipeline for running text, xml, email, and html doc tests
against the library installed without extras
* Create CI Pipeline for running each library extra against their
respective tests
