Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable.
It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you.
However you choose to contribute, please be mindful and respect our code of conduct.
This guide was heavily inspired by the awesome scikit-learn guide to contributing.
There are several ways you can contribute to text-embeddings-inference.
- Fix outstanding issues with the existing code.
- Submit issues related to bugs or desired new features.
- Contribute to the examples or to the documentation.
All contributions are equally valuable to the community. 🥰
If you notice an issue with the existing code and have a fix in mind, feel free to start contributing and open a Pull Request!
Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback.
The text-embeddings-inference library is robust and reliable thanks to users who report the problems they encounter.
Before you report an issue, we would really appreciate it if you could make sure the bug was not already reported (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code.
Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it:
- Your OS type and version, as well as your environment versions (versions of rust, python, and dependencies).
- A short, self-contained, code snippet that allows us to reproduce the bug.
- The full traceback if an exception is raised.
- Attach any other additional information, like screenshots, you think may help.
If there is a new feature you'd like to see in text-embeddings-inference, please open an issue and describe:
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What is the motivation behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community?
Whatever it is, we'd love to hear about it!
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Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you.
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Provide a code snippet that demonstrates the feature's usage.
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If the feature is related to a paper, please include a link.
If your issue is well written we're already 80% of the way there by the time you create it.
We have added templates to help you get started with your issue.
New models are constantly released and if you want to implement a new model, please provide the following information:
- A short description of the model and a link to the paper.
- Link to the implementation if it is open-sourced.
- Link to the model weights if they are available.
If you are willing to contribute the model yourself, let us know so we can help you add it to text-embeddings-inference!
We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested!
TGI is a project led and managed by Hugging Face as it powers our internal services. However, we are happy to have motivated individuals from other organizations join us as maintainers with the goal of making TGI the best inference service.
If you are such an individual (or organization), please reach out to us and let's collaborate.