Try RAG with MariaDB Vector on your own MariaDB data!

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.

Announcing the MariaDB Vector Bounty Program!

Today, we are excited to announce a new fund to help give MariaDB Vector a high-quality integration into as many LLM frameworks as possible. This means that you can get rewarded for integrating MariaDB Vector into a known framework! This program will run until the end of February 2025.

How it will work

  1. Pick a framework: You need to pick one of the frameworks from the list curated by Qdrant that you would like to work on adding MariaDB Vector support to.
  2. Contact us: Contact us on the MariaDB Zulip, in the General channel, just create a topic.

Finally here: MariaDB Vector Preview!

We’re here, we’re open source, and we have RDBMS based Vector Search for you! With the release of MariaDB 11.6 Vector Preview, the MariaDB Server ecosystem can finally check out how the long-awaited Vector Search functionality of MariaDB Server works. The effort is a result of collaborative work by employees of MariaDB plc, MariaDB Foundation and contributors, particularly from Amazon AWS. 

Previously on “MariaDB Vector”

If you’re new to Vector, this is what’s happened so far:

The main point: MariaDB Vector is ready for experimentation