Author Archives: Robert Silén
At MariaDB Day in February in Brussels, VP Engineering Vicențiu Ciurbaru delivered an inspiring presentation titled “Launch Your Open Source Career: First Steps in Contributing to MariaDB.”
In the spirit of the recent positive stats on new contributions to MariaDB, let me recap Vicentiu’s tips for anyone looking to make their first contribution.
Contributing to MariaDB is not only about writing code—it’s about joining a vibrant community where every contribution, big or small, drives innovation. We hope newcomers can see a tangible path to getting involved.
How to start contributing?
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The ideation phase of the MariaDB AI RAG Hackathon is nearing its deadline on Monday (by end of March).
We have several cool submissions so far. One is about combining the Knowledge Graph and LLMs, using MariaDB Vector Nearest Neighbour Search. Another one is about an “advanced context diff”, identifying the differences between two text corpuses based not on their literal wording, but on their content.
All of the current submissions are in the Innovation track. We would particularly like submissions in the Integration track – to add MariaDB to frameworks such as these, or other apps.
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MariaDB has had a voting feature in its issue tracker Jira since the dawn of time, but it hasn’t got much active attention. Despite that, there are now many Jira community items that have collected a fair amount of votes over the years.
Many items are now in limbo—not on the MariaDB road map, but not rejected either. We would like to better understand how to act on these.
More votes, and preferably more detailed comments on syntax and desired functionality or insights on use cases, would help the MariaDB Foundation and Corporation a lot in deciding what to do and how to prioritize resources.
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One week left to join the AI RAG Hackathon with MariaDB Vector and Python!
Winners get to demo at the Helsinki Python meetup in May, receive merit and publicity from MariaDB Foundation and Open Ocean Capital, and prizes from Finnish verkkokauppa.com.
To participate, gather a team (1-5 people) and submit an idea by the end of March for one of the two tracks. You then have until May 5th to develop the idea before the meetup 27th May.
- Integration track: Enable MariaDB Vector in an existing open source project or AI-framework.
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We are excited to announce a hackathon with MariaDB Vector and Python. Since we are reaching outside our bubble, let’s start from the beginning:
MariaDB, the open-source database powers the world’s most demanding applications, from Wikipedia to global financial institutions. Now MariaDB Vector is bringing AI-ready vector search natively into the open-source database world. MySQL users ahoy:
Our hackathon is your chance to explore AI possibilities with MariaDB Vector and Python. Whether you’re a developer, data scientist, or AI enthusiast, MariaDB Foundation invites you to build innovative AI applications, compete for prizes, and showcase your work.
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Continue reading “Join our AI Hackathon with MariaDB Vector”
Still unsure about attending the MariaDB Day in Brussels on Saturday? Here are four reasons to turn up at our FOSDEM Fringe event.
1. Discover blazingly fast Vector search
Learn how to power your latest AI app with MariaDB Vector. Mark Callaghan has been testing its performance and the results are amazingly fast compared to Postgres’ pgvector. Check out part 1, part 2 and part 3 of his blog.
2. Meet the people behind MariaDB
At MariaDB Day you will find several people from MariaDB Foundation, Company and partners: Sergei Golubchik, Vicențiu Ciorbaru, Otto Kekalainen, Peter Zaitsev, Kristian Nielsen, Roman Nozdrin, Jags Ramnarayan, Diego Dupin and Kaj Arnö.
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Continue reading “Four reasons to visit MariaDB Day in Brussels”
The day has come that you have been waiting for since the ChatGPT hype began: You can now build creative AI apps using your own data in MariaDB Server! By creating embeddings of your own data and storing them in your own MariaDB Server, you can develop RAG solutions where LLMs can efficiently execute prompts based on your own specific data as context.
Why RAG?
Retrieval-Augmented Generation (RAG) creates more accurate, fact-based GenAI answers based on data of your own choice, such as your own manuals, articles or other text corpses. RAG answers are more accurate and fact-based than general Large Language Models (LLM) without having to train or fine-tune a model.
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Continue reading “Try RAG with MariaDB Vector on your own MariaDB data!”
MariaDB Vector preview was recently released, bringing much awaited Vector Search functionality to MariaDB Server. One of the major open source contributors to MariaDB Vector has been Amazon. To share the excitement and get an inside view about what it’s like to contribute to MariaDB Server, I had a chat with software engineer Hugo Wen on the Amazon RDS team.
Hugo’s contributions to MariaDB Vector
Hugo Wen’s work on vector similarity search in MariaDB and MySQL started when Amazon’s leadership identified Vector Search functionality as a critical addition and decided to invest Amazon RDS team’s time on contributing to MariaDB Vector.
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