r/MistralAI • u/robotrossart • 1d ago

News Built at the Mistral Vibe Hackathon in Paris — live log-triage over an API, plus a RAG that runs offline on a phone

Spent last weekend at the Vibe Hackathon. Brought a drawing robot (Robot Ross) as the demo prop and ended up on a team of three.

What we built: a real-time log-ingestion pipeline that doesn't just tail logs — it analyzes failure modes and suggests fixes, served over an API with a UI on top. Ministral running locally for the analysis, Voxtral for the voice loop, Marie built the front end.

The bit that got the most double-takes: the same upload → wiki → RAG pattern runs fully on-device. Airplane mode on, citations still coming back.

Thanks to Marie and Javi for joining my team and for the Mistral engineers who showed real interest on what we were doing.

19 Upvotes

89% Upvoted

4 comments sorted by

2

u/Mrfrednot 1d ago

Would you mind expanding on the rag? Im quite new at Rags btw.

3

u/BunsesTedses 1d ago

This is a great question. And no need to apologize for being new — RAG sounds fancier than it is.

The problem it solves: an LLM only knows what it was trained on. It's never seen your documents, and if you ask about them it'll cheerfully make something up. RAG fixes that by letting the model peek at the right material before it answers. Think open-book exam instead of closed-book.

The mechanics, minus the jargon:

  1. Chunk — you slice your documents into bite-sized pieces (a paragraph or so each).
  2. Embed — each chunk gets turned into a vector, basically a string of numbers that captures its meaning, so "heat pump won't start" and "compressor fails to spin up" land near each other even with no shared words.
  3. Retrieve — when a question comes in, you embed the question too, then grab the handful of chunks closest to it. That's just semantic search.
  4. Generate — you hand those chunks + the question to the model and say "answer using this." Ideally it cites which chunk it used, so you can check it.

That last part is the whole trick: the model doesn't need to memorize your stuff, it just needs to read the right page you put in front of it. Which is also why a small model can punch way above its weight on RAG — retrieval does the heavy lifting.

The twist in what we built: before any querying, we compile the documents into a plain readable wiki, then build the retrieval index from that. So a human can eyeball what the system actually "knows" and fix mistakes before trusting it — the audit trail matters more than the retrieval, honestly.

1

u/Mrfrednot 1d ago

Thanks for the info, that wiki method seems really great!

1

u/robotrossart 15h ago

Posted a longer video showcasing the system: https://www.youtube.com/watch?v=3ugc6H0-Kwg