### Description
Currently the CI caches the CI dependencies but uses the hash of all
files in `requirements/`. This isn't completely accurate since the
ingest dependencies are installed in a later step and don't affect the
cached environment. As part of this PR:
* ingest dependencies were isolated into their own folder in
`requirements/ingest/`
* A new cache setup was introduced in the CI to restore the base cache
-> install ingest dependencies -> cache it with a new id
* new make target created to install all ingest dependencies via `pip
install -r ...`
* updates to Dockerfile to use `find ...` to install all dependencies,
avoiding the need to update this when new deps are added.
* update to pip-compile script to run over all `*.in` files in
`requirements/`
### Description
As we add more and more steps to the pipeline (i.e. chunking, embedding,
table manipulation), it would help seperate the responsibility of each
of these into their own processes, running each in parallel using json
files to share data across. This will also help guarantee data is
serializable if this code was used in an actual pipeline. Following is a
flow diagram of the proposed changes. As part of this change:
* A parent pipeline class will be responsible for running each `node`,
which can optionally be run via multiprocessing if it supports it, or
not. Possible nodes at this moment:
* Doc factory: creates all the ingest docs via the source connector
* Source: reads/downloads all of the content to process to the local
filesystem to the location set by the `download_dir` parameter.
* Partition: runs partition on all of the downloaded content in json
format.
* Any number of reformat nodes that modify the partitioned content. This
can include chunking, embedding, etc.
* Write: push the final json into the destination via the destination
connector
* This pipeline relies on the information of the ingest docs to be
available via their serialization. An optimization was introduced with
the `IngestDocJsonMixin` which adds in all the `@property` fields to the
serialized json already being created via the `DataClassJsonMixin`
* For all intermediate steps (partitioning, reformatting), the content
is saved to a dedicated location on the local filesystem. Right now it's
set to `$HOME/.cache/unstructured/ingest/pipeline/STEP_NAME/`.
* Minor changes: made sense to move some of the config parameters
between the read and partition configs when I explicitly divided the
responsibility to download vs partition the content in the pipeline.
* The pipeline class only makes the doc factory, source and partition
nodes required, keeping with the logic that has been supported so far.
All reformatting nodes and write node are optional.
* Long term, there should also be some changes to the base configs
supported by the CLI to support pipeline specific configs, but for now
what exists was used to minimize changes in this PR.
* Final step to copy the final output to the location designated by the
`_output_filename` value of the ingest doc.
* Hashing occurs at each step by hashing the parameters of that step
(i.e. partition configs) along with the previous step via the filename
used. This allows each step to be the same _if_ all the parameters for
it have not changed and the content so far is the same.
* The only data that is shared and has writes to across processes is the
dictionary of ingest json data. This dict is created using the
`multiprocessing.manager.DictProxy` to make sure any interaction with it
is behind a lock.
### Minor refactors included:
* Utility methods added to extract configs from the click options
* Utility method to add common options to click commands.
* All writers moved to using the class approach which extracts a lot of
the common code so there's less copy-paste when new runners are added.
* Use `@property` for source metadata on base ingest doc to add logic to
call `update_source_metadata` if it's still `None` at the time it's
fetched.
### Additional bug fixes included
* Fsspec connectors were not serializable due to the `ingest_doc_cls`.
This was removed from the fields captured by the `@dataclass` decorator
and added in a `__post_init__` method.
* Various reddit connector params were missing. This doesn't have an
explicit ingest test at the moment so was never caught.
* Fsspec connector had the parent `update_source_metadata` misnamed as
`update_source_metadata_metadata` so it was never being called.
### Flow Diagram
### Description
* Add ingest test for Notion docs
* Update default cache dir for connectors to include connector name.
Makes debugging the cached content easier.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: rbiseck3 <rbiseck3@users.noreply.github.com>
* split dependencies by document type
* make pip-compile with new requirements
* add extra requirements to setup.py
* add in all docs; re pip-compile
* extra for all docs
* add pandas to xlsx
* dependency requires for tsv and csv
* handling for doc, docx and odt
* dependency check for pypandoc
* required dependencies for pandoc files
* xml and html
* markdown
* msg
* add in pdf
* add in pptx
* add in excel
* add lxml as base req
* extra all docs for local inference
* local inference installs all
* pin pillow version
* fixes for plain text tests
* fixes for doc
* update make commands
* changelog and version
* add xlrd
* update pip-compile
* pin numpy for python 3.8 support
* more constraints
* contraint on scipy
* update install docs
* constrain ipython
* add outlook to pip-compile
* more ipython constraints
* add extras to dockerfile
* pin office365 client
* few doc tweaks
* types as strings
* last pip-compile
* re pip-comple
* make tidy
* make tidy
* build from Rocky linux unstructured base image
* add qemu for arm
* comment out push while testing
* remove quotes
* Add arch
* bump login action
* add ARCH env var to the push step
* run only subset of tests on arm image
Tests on emulated arm are extremely slow. Likelyhood of something breaking in arm image only, is minimal. I say that knowing I likely just jinxed us.
* re-enable push from main
* add a dnf cleanup
* version bump
* move from dev to minor version bump
Updated to the the latest version of unstructured-inference. detectron2 now gets implemented with onnxruntime, yay!
---------
Co-authored-by: Matt Robinson <mrobinson@unstructured.io>
* docker works
* more epub tests
* changelog version
* support epub + odt + rtf
* update dockerfile
* revert..
* install pandoc on ci env
* pandoc docker grab bashed on arch
* move arch into image
* move back to base image
* Fixes issue where detectron2 would not install on OSX
Tested on Apple silicon based MacBook Pro. This installs tensorboard which is required on OSX and arm based cpu’s for detectron2.
* Improve Arch detection for tensorboard
* remove makefile from commands in readme
pin tensorboard version
This connector takes a slack channel id, token and other options to
pull conversation history for a channel and store it as a text file that
is then processed by unstructured into expected output.
The mailcap centos7 package provides the file /etc/mime.types, which is used by the mimetypes python package. That said, the unstructured code base does not make much use of this but the upstream unstructured-api does.
Bonus: docx mimetype added in lookup table.
* add python-magic
* first pass on filetype detection
* tests for filetype detection
* more tests for file detection
* added tests for error conditions
* install libmagic dev in github
* libmagic install instructions
* pattern for checking email files
* support reading .eml in rb mode
* add auto partition function
* auto tests for emal
* auto tests for docx
* added tests for html
* add pdf and html tests
* linting, linting, linting
* added docs for auto partitioning
* update readme with generic partition brick
* bumped version
* added test for bad type
* detect .docx files from application/octet-stream
* linting, linting, linting
* identify xlsx from octet stream
* install poppler in ci
* fix mocks; test for unknown type
* install poppler utils
* install in one line
* only poppler-utils
* file extension logic from application/octet-stream
* install local inference for ci
* install detectron2
* removing unused dockerfile
