Tag Archives: RAG
Mirror, mirror on the wall — what do you measure when you measure us all?
Is it skill, or is it voice?
Is it code, or is it conversation?
DB-Engines is not a looking glass of perfection, but a mirror of perception — reflecting the chorus of those who search, speak, teach, compare, and build. And like every enchanted mirror, it shows not only what is, but what the world believes it sees.
MariaDB today stands firmly among the world’s top relational databases.
Not by inheritance, and not by illusion, but by the millions who use it, trust it, and shape it.
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Continue reading “Mirror, Mirror on DB-Engines: The MariaDB Story”
We continue our blog series on learning more about users of MariaDB. Searching LinkedIn for posts about MariaDB this morning we saw an impressive confident post about using MariaDB in a RAG solution named SemantiQ. We got curious about it and reached out to the author Lorenzo Cremonese to have a chat.
Tell us about yourself Lorenzo!
I’m an Italian studying in Spain. I began programming when I was 14, and I’m now 22. I’m a self-taught web developer since 3-4 years ago, and I’m now doing a two year University education in Spain focused on web development at the institute IES ENRIC VALOR, in Pego.
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Continue reading “Can you do RAG with Full Text Search in MariaDB?”
Last week we announced the winners of the MariaDB AI RAG hackathon that we organized together with the Helsinki Python meetup group. Now is time to go for a deep dive into the innovation track winner. Dymtro Abramov put together an end to end RAG application for making technical meetup videos searchable with semantic relevance (based on their caption texts). We were impressed by the idea and the implementation that is definitely beyond just a proof of concept for AI RAG with MariaDB Vector.
Dmytro, tell us about yourself and why you decided to join the Hackathon?
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We recently announced the winners of the MariaDB AI RAG hackathon that we organized together with the Helsinki Python meetup group. Let’s deep dive into the integration track winner. David Ramos chose to contribute a MariaDB integration for MCP Server. MariaDB plc was impressed by the results and has picked it up for further development with more features.
David, tell us about yourself and why you decided to join the Hackathon?
I am an aspiring Data Scientist from Colombia. I studied Physics in college, but by the time I graduated, I realized what I enjoyed the most was working with data and programming, so I decided to make the shift to Data Science.
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The Helsinki Python meetup on Tuesday 27th May was hosted by MariaDB Foundation with nearly 100 participants of the 2600+ members of the Helsinki python meetup community.
In addition to being a successful opportunity to mingle and learn python together, we finally got to announce the winners of the MariaDB AI RAG Hackathon! (winners posing in the feature photo of the blog)
Presentations: AI and MariaDB
We welcomed participants of the meetup with a brief introduction to what MariaDB is as a software, and the sponsoring parties: MariaDB Foundation, Open Ocean Capital and verkkokauppa.com.
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Continue reading “Helsinki Python meetup with AI RAG and MariaDB Foundation”
On Monday we had our final submission day for the MariaDB AI RAG Hackathon. We got several high quality replies in both tracks: integration and innovation.
The quality was so high that we are keen to replicate this hackathon in other locations and are currently looking for cooperation partners. We are truly grateful to the Helsinki Python meetup group without which this would not have been possible. They have a great community, organise excellent events, and played a key role in making the hackathon happen.
We are now reviewing the submissions together with the Helsinki Python meetup group.
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Continue reading “Impressive Submissions at the MariaDB AI RAG Hackathon”
We had the pleasure on Friday to take individual calls with everybody who submitted to the ideation phase of the MariaDB AI RAG hackathon.
The ideation phase deadline passed last week, and we are happy to share that we received several promising submissions for both the innovation track and the integration track. Innovation involves applications using MariaDB Vector, like RAG, and integration being enabling MariaDB Vector in an existing framework.
Participants range from individual contributors to even a corporate team. Some already have some experience with AI, and some are newcomers to RAG.
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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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