[GSoC 25][Blog Post]: Beam ML Vector DB/Feature Store Project

[mohamedawnallah]

Please add a meaningful description for your change here

---

Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

• Mention the appropriate issue in your description (for example: addresses #123), if applicable. This will automatically add a link to the pull request in the issue. If you would like the issue to automatically close on merging the pull request, comment fixes #<ISSUE NUMBER> instead.
• Update CHANGES.md with noteworthy changes.
• If this contribution is large, please file an Apache Individual Contributor License Agreement.

See the Contributor Guide for more tips on how to make review process smoother.

To check the build health, please visit https://github.com/apache/beam/blob/master/.test-infra/BUILD_STATUS.md

GitHub Actions Tests Status (on master branch)

See CI.md for more information about GitHub Actions CI or the workflows README to see a list of phrases to trigger workflows.

[mohamedawnallah]

/gemini review

[gemini-code-assist]

Summary of Changes

Hello @mohamedawnallah, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new blog post that comprehensively documents the author's Google Summer of Code 2025 project. The project's core focus was to significantly enhance Apache Beam's Python SDK by integrating connectors for various machine learning components, specifically vector databases like Milvus and feature stores such as Tecton, aiming to bolster support for advanced ML workflows. The PR also includes the necessary author metadata to correctly attribute the new blog content.

Highlights

• New Blog Post Added: Introduced a new blog post titled "Google Summer of Code 2025 - Beam ML Vector DB/Feature Store integrations" to document the author's GSoC journey and project outcomes.
• GSoC Project Summary: Detailed the GSoC project's objective of enhancing Beam's Python SDK with connectors for vector databases (Milvus) and feature stores (Tecton) to support ML use cases like RAG and feature engineering.
• Challenges and Learnings: Provided insights into the challenges encountered during the GSoC project, such as Docker TestContainers compatibility and CI workflow triggering, along with lessons learned and tips for future contributors.
• Author Attribution: Added the author's profile, 'mohamedawnallah', to the 'authors.yml' file, enabling proper attribution for the new blog post.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request adds a new blog post from a GSoC contributor about their project on Beam ML connectors. The post is well-structured and provides a good overview of the work, challenges, and learnings. My review includes several suggestions to improve grammar, clarity, and formatting in the markdown file to enhance readability for the audience. These include correcting verb tenses, fixing broken links, and rephrasing ambiguous or verbose sentences.

[mohamedawnallah]

Willing to follow-up shortly with a PR to update the CONTRIBUTING.md file to include reference to workflows README file so that future contributors can avoid this challenge

[mohamedawnallah]

#36303

[github-actions]

Assigning reviewers:

R: @liferoad for label website.

Note: If you would like to opt out of this review, comment assign to next reviewer.

Available commands:

• stop reviewer notifications - opt out of the automated review tooling
• remind me after tests pass - tag the comment author after tests pass
• waiting on author - shift the attention set back to the author (any comment or push by the author will return the attention set to the reviewers)

The PR bot will only process comments in the main thread (not review comments).

[mohamedawnallah]

cc: @damccorm

[damccorm]

Thank you - this is well written and a good final artifact! I just had one suggestion, but otherwise this looks great to me.