Create language model based on Uyuni documentation
an idea by nadvornik
Updated about 2 years ago. No love. 1 follower. Has no hacker: grab it!

Project Description

The goal is to have a language model, that is able to answer technical questions on Uyuni. Uyuni documentation is too large for in-context processing, so finetuning is the way to go.

Goal for this Hackweek

Finetune a model based on llama-2-7b.

Resources

github repo

No Hackers yet

Join this project

Looking for hackers with the skills:

ai uyuni

This project is part of:

Hack Week 23

Activity

  • about 2 years ago: nadvornik added keyword "ai" to this project.
  • about 2 years ago: nadvornik added keyword "uyuni" to this project.
  • about 2 years ago: nadvornik originated this project.


    • Comments

    Be the first to comment!

    Similar Projects

    MCP Server for SCC by digitaltomm

    Description

    Provide an MCP Server implementation for customers to access data on scc.suse.com via MCP protocol. The core benefit of this MCP interface is that it has direct (read) access to customer data in SCC, so the AI agent gets enhanced knowledge about individual customer data, like subscriptions, orders and registered systems.

    Architecture

    Schema

    Goals

    We want to demonstrate a proof of concept to connect to the SCC MCP server with any AI agent, for example gemini-cli or codex. Enabling the user to ask questions regarding their SCC inventory.

    For this Hackweek, we target that users get proper responses to these example questions:

    • Which of my currently active systems are running products that are out of support?
    • Do I have ready to use registration codes for SLES?
    • What are the latest 5 released patches for SLES 15 SP6? Output as a list with release date, patch name, affected package names and fixed CVEs.
    • Which versions of kernel-default are available on SLES 15 SP6?

    Technical Notes

    Similar to the organization APIs, this can expose to customers data about their subscriptions, orders, systems and products. Authentication should be done by organization credentials, similar to what needs to be provided to RMT/MLM. Customers can connect to the SCC MCP server from their own MCP-compatible client and Large Language Model (LLM), so no third party is involved.

    Milestones

    [x] Basic MCP API setup
  MCP endpoints
  [x] Products / Repositories
  [x] Subscriptions / Orders 
  [x] Systems
  [x] Packages
[x] Document usage with Gemini CLI, Codex

    Resources

    Gemini CLI setup:

    ~/.gemini/settings.json:

    Backporting patches using LLM by jankara

    Description

    Backporting Linux kernel fixes (either for CVE issues or as part of general git-fixes workflow) is boring and mostly mechanical work (dealing with changes in context, renamed variables, new helper functions etc.). The idea of this project is to explore usage of LLM for backporting Linux kernel commits to SUSE kernels using LLM.

    Goals

    • Create safe environment allowing LLM to run and backport patches without exposing the whole filesystem to it (for privacy and security reasons).
    • Write prompt that will guide LLM through the backporting process. Fine tune it based on experimental results.
    • Explore success rate of LLMs when backporting various patches.

    Resources

    • Docker
    • Gemini CLI

    Repository

    Current version of the container with some instructions for use are at: https://gitlab.suse.de/jankara/gemini-cli-backporter

    GenAI-Powered Systemic Bug Evaluation and Management Assistant by rtsvetkov

    Motivation

    What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?

    Let's make GenAI look on the bug from the systemic point and evaluate what we don't know. Which piece of information is missing to take a decision?

    Description

    To build a tool that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style or Systemic questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.

    Goals

    Set up a Python environment

    Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).

    Build the Dialogue Loop

    1. Write a basic Python script using the Gemini API.
    2. Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation

    Socratic/Systemic Strategy Implementation

    1. Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
    2. Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
    3. Implement Bugzillla call to collect the
    4. Implement Questioning Framework as LLVM pre-conditioning
    5. Define set of instructions
    6. Assemble the Tool

    Resources

    What are Systemic Questions?

    Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
    In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.

    Gitlab Project

    gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question

    Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

    Self-Scaling LLM Infrastructure Powered by Rancher

    logo

    Description

    The Problem

    Running LLMs can get expensive and complex pretty quickly.

    Today there are typically two choices:

    1. Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
    2. Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.

    What if there was a middle ground?

    What if infrastructure scaled itself instead of making you scale it?

    Can we use existing Rancher capabilities like CAPI, autoscaling, and GitOps to make this simpler instead of building everything from scratch?

    Project Repository: github.com/alexander-demicev/llmserverless

    What This Project Does

    A key feature is hybrid deployment: requests can be routed based on complexity or privacy needs. Simple or low-sensitivity queries can use public APIs (like OpenAI), while complex or private requests are handled in-house on local infrastructure. This flexibility allows balancing cost, privacy, and performance - using cloud for routine tasks and on-premises resources for sensitive or demanding workloads.

    A complete, self-scaling LLM infrastructure that:

    • Scales to zero when idle (no idle costs)
    • Scales up automatically when requests come in
    • Adds more nodes when needed, removes them when demand drops
    • Runs on any infrastructure - laptop, bare metal, or cloud

    Think of it as "serverless for LLMs" - focus on building, the infrastructure handles itself.

    How It Works

    A combination of open source tools working together:

    Flow:

    • Users interact with OpenWebUI (chat interface)
    • Requests go to LiteLLM Gateway
    • LiteLLM routes requests to:
      • Ollama (Knative) for local model inference (auto-scales pods)
      • Or cloud APIs for fallback

    Explore LLM evaluation metrics by thbertoldi

    Description

    Learn the best practices for evaluating LLM performance with an open-source framework such as DeepEval.

    Goals

    Curate the knowledge learned during practice and present it to colleagues.

    -> Maybe publish a blog post on SUSE's blog?

    Resources

    https://deepeval.com

    https://docs.pactflow.io/docs/bi-directional-contract-testing

    Set Uyuni to manage edge clusters at scale by RDiasMateus

    Description

    Prepare a Poc on how to use MLM to manage edge clusters. Those cluster are normally equal across each location, and we have a large number of them.

    The goal is to produce a set of sets/best practices/scripts to help users manage this kind of setup.

    Goals

    step 1: Manual set-up

    Goal: Have a running application in k3s and be able to update it using System Update Controler (SUC)

    • Deploy Micro 6.2 machine

    • Deploy k3s - single node

      • https://docs.k3s.io/quick-start

    • Build/find a simple web application (static page)

      • Build/find a helmchart to deploy the application

    • Deploy the application on the k3s cluster

    • Install App updates through helm update

    • Install OS updates using MLM

    step 2: Automate day 1

    Goal: Trigger the application deployment and update from MLM

    • Salt states For application (with static data)
      • Deploy the application helmchart, if not present
      • install app updates through helmchart parameters
    • Link it to GIT
      • Define how to link the state to the machines (based in some pillar data? Using configuration channels by importing the state? Naming convention?)
      • Use git update to trigger helmchart app update
    • Recurrent state applying configuration channel?

    step 3: Multi-node cluster

    Goal: Use SUC to update a multi-node cluster.

    • Create a multi-node cluster
    • Deploy application
      • call the helm update/install only on control plane?
    • Install App updates through helm update
    • Prepare a SUC for OS update (k3s also? How?)
      • https://github.com/rancher/system-upgrade-controller
      • https://documentation.suse.com/cloudnative/k3s/latest/en/upgrades/automated.html
      • Update/deploy the SUC?
      • Update/deploy the SUC CRD with the update procedure

    Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios

    Description

    Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.

    This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.

    The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.

    Goals

    By the end of Hack Week, we aim to have a single, working Python script that:

    1. Connects to Prometheus and executes a query to fetch detailed test failure history.
    2. Processes the raw data into a format suitable for the Gemini API.
    3. Successfully calls the Gemini API with the data and a clear prompt.
    4. Parses the AI's response to extract a simple list of flaky tests.
    5. Saves the list to a JSON file that can be displayed in Grafana.
    6. New panel in our Dashboard listing the Flaky tests

    Resources

    Outcome

    Enhance setup wizard for Uyuni by PSuarezHernandez

    Description

    This project wants to enhance the intial setup on Uyuni after its installation, so it's easier for a user to start using with it.

    Uyuni currently uses "uyuni-tools" (mgradm) as the installation entrypoint, to trigger the installation of Uyuni in the given host, but does not really perform an initial setup, for instance:

    • user creation
    • adding products / channels
    • generating bootstrap repos
    • create activation keys
    • ...

    Goals

    • Provide initial setup wizard as part of mgradm uyuni installation

    Resources

    Set Up an Ephemeral Uyuni Instance by mbussolotto

    Description

    To test, check, and verify the latest changes in the master branch, we want to easily set up an ephemeral environment.

    Goals

    • Create an ephemeral environment manually

    • Create an ephemeral environment automatically

      Resources

    • https://github.com/uyuni-project/uyuni

    • https://www.uyuni-project.org/uyuni-docs/en/uyuni/index.html

    Uyuni Health-check Grafana AI Troubleshooter by ygutierrez

    Description

    This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.

    Goals

    • Investigate if and how the grafana-llm-app plug-in can be used within the Uyuni Health-check tool.
    • Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
    • Evaluate support for local LLMs and external APIs through the plugin.
    • Evaluate if and how the Uyuni MCP server could be integrated as another source of information.

    Resources

    Grafana LMM plug-in

    Uyuni Health-check