### Description
This refactors the current ingest CLI process to support better
granularity in how the steps are ran
* Both multiprocessing and async now supported. Given that a lot of the
steps are IO-bound, such as downloading and uploading content, we can
achieve better parallelization by using async here
* Destination step broken up into a stager step and an upload step. This
will allow for steps that require manipulation of the data between
formats, such as converting the elements json into a csv format to
upload for tabular destinations, to be pulled out of the step that does
the actual upload.
* The process of writing the content to a local destination was now
pulled out as it's own dedicated destination connector, meaning you no
longer need to persist the content locally once the process is done if
the content was uploaded elsewhere.
* Quick update to the chunker/partition step to use the python client.
* Move the uncompress suppport as a pipeline step since this can
arbitrarily apply to any concrete files that have been downloaded,
regardless of where they came from.
* Leverage last modified date to mark files to be reprocessed, even if
the file already exists locally.
### Callouts
Retry configs haven't been moved over yet. This is an open question
because the intent was for it to wrap potential connection errors but
now any of the other steps that leverage an API might run into network
connection issues. Should those be isolated in each of the steps and
wrapped with the same retry configs? Or do we need to expose a unique
retry config for each step? This would bloat the input params even more.
### Testing
* If you want to run the new code as an SDK, there's an example file
that was added to highlight how to do that:
[example.py](https://github.com/Unstructured-IO/unstructured/blob/roman/refactor-ingest/unstructured/ingest/v2/example.py)
* If you want to run the new code as an isolated CLI:
```shell
PYTHONPATH=. python unstructured/ingest/v2/main.py --help
```
* If you want to see which commands have been migrated to the new
version, there's now a `v2` short help text next to those commands when
running the current cli:
```shell
PYTHONPATH=. python unstructured/ingest/main.py --help
Usage: main.py [OPTIONS] COMMAND [ARGS]...main.py --help
Options:
--help Show this message and exit.
Commands:
airtable
azure
biomed
box
confluence
delta-table
discord
dropbox
elasticsearch
fsspec
gcs
github
gitlab
google-drive
hubspot
jira
local v2
mongodb
notion
onedrive
opensearch
outlook
reddit
s3 v2
salesforce
sftp
sharepoint
slack
wikipedia
```
You can run any of the local or s3 specific ingest tests and these
should now work.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: rbiseck3 <rbiseck3@users.noreply.github.com>
Thanks to @erichare from AstraDB
Adds support for specifying the indexing options for various columns in
Astra DB, allowing users to avoid a situation where long text columns
are by-default indexed.
Changes to: test_unstructured_ingest/python/test-ingest-astra-output.py
are forward looking from AstraDB
This PR:
- Fixes occasional collection deletion failures for AstraDB via putting
collection deletion statements inside a trap statement. It uses click
commands to do this.
Testing:
- Run ingest astradb destination test
Thanks to Eric Hare @erichare at DataStax we have a new destination
connector.
This Pull Request implements an integration with [Astra
DB](https://datastax.com) which allows for the Astra DB Vector Database
to be compatible with Unstructured's set of integrations.
To create your Astra account and authenticate with your
`ASTRA_DB_APPLICATION_TOKEN`, and `ASTRA_DB_API_ENDPOINT`, follow these
steps:
1. Create an account at https://astra.datastax.com
2. Login and create a new database
3. From the database page, in the right hand panel, you will find your
API Endpoint
4. Beneath that, you can create a Token to be used
Some notes about Astra DB:
- Astra DB is a Vector Database which allows for high-performance
database transactions, and enables modern GenAI apps [See
here](https://docs.datastax.com/en/astra/astra-db-vector/get-started/concepts.html)
- It supports similarity search via a number of methods [See
here](https://docs.datastax.com/en/astra/astra-db-vector/get-started/concepts.html#metrics)
- It also supports non-vector tables / collections
