https://github.com/deepset-ai/document-store

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
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Scientific vocabulary similarity
Low similarity (16.2%) to scientific vocabulary

Last synced: 8 months ago · JSON representation

Repository

Basic Info

Host: GitHub
Owner: deepset-ai
License: apache-2.0
Language: Python
Default Branch: main
Size: 52.7 KB

Statistics

Stars: 8
Watchers: 3
Forks: 6
Open Issues: 1
Releases: 0

Created almost 3 years ago · Last pushed about 2 years ago

Metadata Files

Readme License

Example Store

This Github repository is a template that can be used to create custom document stores to extend the new Haystack API available from version 2.0.

Template features

By creating a new repo using this template, you'll get the following advantages: - Ready-made code layout and scaffold to build a custom document store. - Support for packaging and distributing the code through Python wheels using Hatch. - Github workflow to build and upload a package when tagging the repo. - Github workflow to run the tests on Pull Requests.

How to use this repo

Create a new repository starting from this template. If you never used this feature before, you can find more details in Github docs.
If possible, follow the convention technology-haystack for the name of the new repository, where technology can be for example the name of a vector database you're integrating.
Rename the package src/example_store to something more meaningful and adjust the Python import statements.
Replace any occurrence of example_store and example-store across the repo, according to the name you chose in the previous steps.
Search the whole codebase for the string #FIXME, that's where you're supposed to change or add code specific for the database you're integrating.
If Apache 2.0 is not suitable for your needs, change the software license.

When your custom document store is ready and working, feel free to add it to the list of available Haystack Integrations by opening a Pull Request in this repo.

Test

You can use hatch to run the linters:

console ~$ hatch run lint:all cmd [1] | ruff . cmd [2] | black --check --diff . All done! ✨ 🍰 ✨ 6 files would be left unchanged. cmd [3] | mypy --install-types --non-interactive src/example_store tests Success: no issues found in 6 source files

Similar for running the tests:

console ~$ hatch run cov cmd [1] | coverage run -m pytest tests ...

Build

To build the package you can use hatch:

```console ~$ hatch build [sdist] dist/example_store-0.0.1.tar.gz

[wheel] dist/example_store-0.0.1-py3-none-any.whl ```

Release

To automatically build and push the package to PyPI, you need to set a repository secret called PYPI_API_TOKEN containing a valid token for your PyPI account. Then set the desired version number in src/example_store/__about__.py and tag the commit using the format vX.Y.Z. After pushing the tag, a Github workflow will start and take care of building and releasing the package.

License

example-store is distributed under the terms of the Apache-2.0 license.

Owner

Name: deepset
Login: deepset-ai
Kind: organization
Email: hello@deepset.ai
Location: Berlin, Germany

Website: https://deepset.ai
Twitter: deepset_ai
Repositories: 14
Profile: https://github.com/deepset-ai

Building enterprise search systems powered by latest NLP & open-source.

GitHub Events

Total

Watch event: 3
Pull request event: 1
Fork event: 3

Last Year

Watch event: 3
Pull request event: 1
Fork event: 3

Issues and Pull Requests

Last synced: over 1 year ago

All Time

Total issues: 0
Total pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Total issue authors: 0
Total pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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