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

    "what is it" file and directory analysis via MCP and local LLM, for console and KDE by rsimai

    Description

    Users sometimes wonder what files or directories they find on their local PC are good for. If they can't determine from the filename or metadata, there should an easy way to quickly analyze the content and at least guess the meaning. An LLM could help with that, through the use of a filesystem MCP and to-text-converters for typical file types. Ideally this is integrated into the desktop environment but works as well from a console. All data is processed locally or "on premise", no artifacts remain or leave the system.

    Goals

    • The user can run a command from the console, to check on a file or directory
    • The filemanager contains the "analyze" feature within the context menu
    • The local LLM could serve for other use cases where privacy matters

    TBD

    • Find or write capable one-shot and interactive MCP client
    • Find or write simple+secure file access MCP server
    • Create local LLM service with appropriate footprint, containerized
    • Shell command with options
    • KDE integration (Dolphin)
    • Package
    • Document

    Resources

    SUSE Observability MCP server by drutigliano

    Description

    The idea is to implement the SUSE Observability Model Context Protocol (MCP) Server as a specialized, middle-tier API designed to translate the complex, high-cardinality observability data from StackState (topology, metrics, and events) into highly structured, contextually rich, and LLM-ready snippets.

    This MCP Server abstract the StackState APIs. Its primary function is to serve as a Tool/Function Calling target for AI agents. When an AI receives an alert or a user query (e.g., "What caused the outage?"), the AI calls an MCP Server endpoint. The server then fetches the relevant operational facts, summarizes them, normalizes technical identifiers (like URNs and raw metric names) into natural language concepts, and returns a concise JSON or YAML payload. This payload is then injected directly into the LLM's prompt, ensuring the final diagnosis or action is grounded in real-time, accurate SUSE Observability data, effectively minimizing hallucinations.

    Goals

    • Grounding AI Responses: Ensure that all AI diagnoses, root cause analyses, and action recommendations are strictly based on verifiable, real-time data retrieved from the SUSE Observability StackState platform.
    • Simplifying Data Access: Abstract the complexity of StackState's native APIs (e.g., Time Travel, 4T Data Model) into simple, semantic functions that can be easily invoked by LLM tool-calling mechanisms.
    • Data Normalization: Convert complex, technical identifiers (like component URNs, raw metric names, and proprietary health states) into standardized, natural language terms that an LLM can easily reason over.
    • Enabling Automated Remediation: Define clear, action-oriented MCP endpoints (e.g., execute_runbook) that allow the AI agent to initiate automated operational workflows (e.g., restarts, scaling) after a diagnosis, closing the loop on observability.

     Hackweek STEP

    • Create a functional MCP endpoint exposing one (or more) tool(s) to answer queries like "What is the health of service X?") by fetching, normalizing, and returning live StackState data in an LLM-ready format.

     Scope

    • Implement read-only MCP server that can:
      • Connect to a live SUSE Observability instance and authenticate (with API token)
      • Use tools to fetch data for a specific component URN (e.g., current health state, metrics, possibly topology neighbors, ...).
      • Normalize response fields (e.g., URN to "Service Name," health state DEVIATING to "Unhealthy", raw metrics).
      • Return the data as a structured JSON payload compliant with the MCP specification.

    Deliverables

    • MCP Server v0.1 A running Golang MCP server with at least one tool.
    • A README.md and a test script (e.g., curl commands or a simple notebook) showing how an AI agent would call the endpoint and the resulting JSON payload.

    Outcome A functional and testable API endpoint that proves the core concept: translating complex StackState data into a simple, LLM-ready format. This provides the foundation for developing AI-driven diagnostics and automated remediation.

    Resources

    • https://www.honeycomb.io/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine
    • https://www.datadoghq.com/blog/datadog-remote-mcp-server
    • https://modelcontextprotocol.io/specification/2025-06-18/index
    • https://modelcontextprotocol.io/docs/develop/build-server

     Basic implementation

    • https://github.com/drutigliano19/suse-observability-mcp-server

    Results

    Successfully developed and delivered a fully functional SUSE Observability MCP Server that bridges language models with SUSE Observability's operational data. This project demonstrates how AI agents can perform intelligent troubleshooting and root cause analysis using structured access to real-time infrastructure data.

    Example execution

    Bugzilla goes AI - Phase 1 by nwalter

    Description

    This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.

    Goals

    To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.

    Project Charter

    Bugzilla goes AI Phase 1

    Description

    Project Achievements during Hackweek

    In this file you can read about what we achieved during Hackweek.

    Project Achievements

    SUSE Edge Image Builder MCP by eminguez

    Description

    Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.

    Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.

    Goals

    • Familiarize myself with MCPs
    • Unrealistic: Have an MCP that can generate an EIB config file

    Resources

    Result

    https://github.com/e-minguez/eib-mcp

    I've extensively used antigravity and its agent mode to code this. This heavily uses https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema for the MCP to be built.

    I've ended up learning a lot of things about "prompting", json schemas in general, some golang, MCPs and AI in general :)

    Example:

    Generate an Edge Image Builder configuration for an ISO image based on slmicro-6.2.iso, targeting x86_64 architecture. The output name should be 'my-edge-image' and it should install to /dev/sda. It should deploy a 3 nodes kubernetes cluster with nodes names "node1", "node2" and "node3" as: * hostname: node1, IP: 1.1.1.1, role: initializer * hostname: node2, IP: 1.1.1.2, role: agent * hostname: node3, IP: 1.1.1.3, role: agent The kubernetes version should be k3s 1.33.4-k3s1 and it should deploy a cert-manager helm chart (the latest one available according to https://cert-manager.io/docs/installation/helm/). It should create a user called "suse" with password "suse" and set ntp to "foo.ntp.org". The VIP address for the API should be 1.2.3.4

    Generates:

    ``` apiVersion: "1.0" image: arch: x86_64 baseImage: slmicro-6.2.iso imageType: iso outputImageName: my-edge-image kubernetes: helm: charts: - name: cert-manager repositoryName: jetstack

    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

    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

    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

    mgr-ansible-ssh - Intelligent, Lightweight CLI for Distributed Remote Execution by deve5h

    Description

    By the end of Hack Week, the target will be to deliver a minimal functional version 1 (MVP) of a custom command-line tool named mgr-ansible-ssh (a unified wrapper for BOTH ad-hoc shell & playbooks) that allows operators to:

    1. Execute arbitrary shell commands on thousand of remote machines simultaneously using Ansible Runner with artifacts saved locally.
    2. Pass runtime options such as inventory file, remote command string/ playbook execution, parallel forks, limits, dry-run mode, or no-std-ansible-output.
    3. Leverage existing SSH trust relationships without additional setup.
    4. Provide a clean, intuitive CLI interface with --help for ease of use. It should provide consistent UX & CI-friendly interface.
    5. Establish a foundation that can later be extended with advanced features such as logging, grouping, interactive shell mode, safe-command checks, and parallel execution tuning.

    The MVP should enable day-to-day operations to efficiently target thousands of machines with a single, consistent interface.

    Goals

    Primary Goals (MVP):

    Build a functional CLI tool (mgr-ansible-ssh) capable of executing shell commands on multiple remote hosts using Ansible Runner. Test the tool across a large distributed environment (1000+ machines) to validate its performance and reliability.

    Looking forward to significantly reducing the zypper deployment time across all 351 RMT VM servers in our MLM cluster by eliminating the dependency on the taskomatic service, bringing execution down to a fraction of the current duration. The tool should also support multiple runtime flags, such as:

    mgr-ansible-ssh: Remote command execution wrapper using Ansible Runner

Usage: mgr-ansible-ssh [--help] [--version] [--inventory INVENTORY]
                   [--run RUN] [--playbook PLAYBOOK] [--limit LIMIT]
                   [--forks FORKS] [--dry-run] [--no-ansible-output]

Required Arguments
--inventory, -i      Path to Ansible inventory file to use

Any One of the Arguments Is Required
--run, -r            Execute the specified shell command on target hosts
--playbook, -p       Execute the specified Ansible playbook on target hosts

Optional Arguments
--help, -h           Show the help message and exit
--version, -v        Show the version and exit
--limit, -l          Limit execution to specific hosts or groups
--forks, -f          Number of parallel Ansible forks
--dry-run            Run in Ansible check mode (requires -p or --playbook)
--no-ansible-output  Suppress Ansible stdout output

    Secondary/Stretched Goals (if time permits):

    1. Add pretty output formatting (success/failure summary per host).
    2. Implement basic logging of executed commands and results.
    3. Introduce safety checks for risky commands (shutdown, rm -rf, etc.).
    4. Package the tool so it can be installed with pip or stored internally.

    Resources

    Collaboration is welcome from anyone interested in CLI tooling, automation, or distributed systems. Skills that would be particularly valuable include:

    1. Python especially around CLI dev (argparse, click, rich)

    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

    Enable more features in mcp-server-uyuni by j_renner

    Description

    I would like to contribute to mcp-server-uyuni, the MCP server for Uyuni / Multi-Linux Manager) exposing additional features as tools. There is lots of relevant features to be found throughout the API, for example:

    • System operations and infos
    • System groups
    • Maintenance windows
    • Ansible
    • Reporting
    • ...

    At the end of the week I managed to enable basic system group operations:

    • List all system groups visible to the user
    • Create new system groups
    • List systems assigned to a group
    • Add and remove systems from groups

    Goals

    • Set up test environment locally with the MCP server and client + a recent MLM server [DONE]
    • Identify features and use cases offering a benefit with limited effort required for enablement [DONE]
    • Create a PR to the repo [DONE]

    Resources