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

    Liz - Prompt autocomplete by ftorchia

    Description

    Liz is the Rancher AI assistant for cluster operations.

    Goals

    We want to help users when sending new messages to Liz, by adding an autocomplete feature to complete their requests based on the context.

    Example:

    • User prompt: "Can you show me the list of p"
    • Autocomplete suggestion: "Can you show me the list of p...od in local cluster?"

    Example:

    • User prompt: "Show me the logs of #rancher-"
    • Chat console: It shows a drop-down widget, next to the # character, with the list of available pod names starting with "rancher-".

    Technical Overview

    1. The AI agent should expose a new ws/autocomplete endpoint to proxy autocomplete messages to the LLM.
    2. The UI extension should be able to display prompt suggestions and allow users to apply the autocomplete to the Prompt via keyboard shortcuts.

    Resources

    GitHub repository

    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

    MCP Trace Suite by r1chard-lyu

    Description

    This project plans to create an MCP Trace Suite, a system that consolidates commonly used Linux debugging tools such as bpftrace, perf, and ftrace.

    The suite is implemented as an MCP Server. This architecture allows an AI agent to leverage the server to diagnose Linux issues and perform targeted system debugging by remotely executing and retrieving tracing data from these powerful tools.

    • Repo: https://github.com/r1chard-lyu/systracesuite
    • Demo: Slides

    Goals

    1. Build an MCP Server that can integrate various Linux debugging and tracing tools, including bpftrace, perf, ftrace, strace, and others, with support for future expansion of additional tools.

    2. Perform testing by intentionally creating bugs or issues that impact system performance, allowing an AI agent to analyze the root cause and identify the underlying problem.

    Resources

    • Gemini CLI: https://geminicli.com/
    • eBPF: https://ebpf.io/
    • bpftrace: https://github.com/bpftrace/bpftrace/
    • perf: https://perfwiki.github.io/main/
    • ftrace: https://github.com/r1chard-lyu/tracium/

    Update M2Crypto by mcepl

    There are couple of projects I work on, which need my attention and putting them to shape:

    Goal for this Hackweek

    • Put M2Crypto into better shape (most issues closed, all pull requests processed)
    • More fun to learn jujutsu
    • Play more with Gemini, how much it help (or not).
    • Perhaps, also (just slightly related), help to fix vis to work with LuaJIT, particularly to make vis-lspc working.

    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

    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

    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)

    Uyuni Saltboot rework by oholecek

    Description

    When Uyuni switched over to the containerized proxies we had to abandon salt based saltboot infrastructure we had before. Uyuni already had integration with a Cobbler provisioning server and saltboot infra was re-implemented on top of this Cobbler integration.

    What was not obvious from the start was that Cobbler, having all it's features, woefully slow when dealing with saltboot size environments. We did some improvements in performance, introduced transactions, and generally tried to make this setup usable. However the underlying slowness remained.

    Goals

    This project is not something trying to invent new things, it is just finally implementing saltboot infrastructure directly with the Uyuni server core.

    Instead of generating grub and pxelinux configurations by Cobbler for all thousands of systems and branches, we will provide a GET access point to retrieve grub or pxelinux file during the boot:

    /saltboot/group/grub/$fqdn and similar for systems /saltboot/system/grub/$mac

    Next we adapt our tftpd translator to query these points when asked for default or mac based config.

    Lastly similar thing needs to be done on our apache server when HTTP UEFI boot is used.

    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

    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