Project Description

Over the years, our bugzilla database has grown up in size, becoming a very valuable source of truth for most support and development cases; still searching for specific items is quite tricky and the results do not always match the expectations.

What about feeding a Maching Learning platform with the Bugzilla Database, in order to be able to query it through AI interface? Wouldn't it be nice/convenient to ask to AI: "Gimme hints about this kernel dump!" or "What is the root cause of this stack trace?"

It is the age of choice in the end, isn't it?

Goal for this Hackweek

For this Hackweek, the focus is to trigger a discussion on the following non-exhaustive list:

  • What are the boundaries to be set when considering such an approach (legal, ethical, technological, whatever)
  • How much of the Bugzilla DB can be used for feeding ML? ( can we use customer's data? what about partner's data?)
  • Find out an open source ML solution fitting our needs;
  • Find out some hardware where the solution can be eventually run on.

Anyone interested can join the discussion on the open Slack channel #discuss-bugzilla-ai

Resources

[1] https://blog.opensource.org/towards-a-definition-of-open-artificial-intelligence-first-meeting-recap/

Looking for hackers with the skills:

ai machinelearning machine-learning bugzilla support

This project is part of:

Hack Week 23

Activity

  • about 1 year ago: wfrisch liked this project.
  • about 1 year ago: jmodak liked this project.
  • about 1 year ago: cxiong liked this project.
  • about 1 year ago: lthadeus liked this project.
  • about 1 year ago: ygutierrez liked this project.
  • about 1 year ago: paolodepa added keyword "machine-learning" to this project.
  • about 1 year ago: paolodepa added keyword "bugzilla" to this project.
  • about 1 year ago: paolodepa added keyword "support" to this project.
  • about 1 year ago: paolodepa added keyword "ai" to this project.
  • about 1 year ago: paolodepa added keyword "machinelearning" to this project.
  • about 1 year ago: paolodepa originated this project.

  • Comments

    • paolodepa
      about 1 year ago by paolodepa | Reply

      Preliminary findings: talking to Amartya Chakraborty, who works to the Rancher AI project (https://github.com/rancher/opni), it seems that their framework can be attached to a Bugzilla instance for machine learning and pobably this will be explorated in the future

    • paolodepa
      about 1 year ago by paolodepa | Reply

      Preliminary finding: the Mozilla foundation is actively working on https://github.com/mozilla/bugbug, coming with very promising features: it's worth to try to setup an instance and feed it using our Bugzilla data.

    • paolodepa
      about 1 year ago by paolodepa | Reply

      Suspended due to flu: feel free to take-over

    • paolodepa
      about 1 year ago by paolodepa | Reply

      Postponed to upcoming hackweeks

    Similar Projects

    Run local LLMs with Ollama and explore possible integrations with Uyuni by PSuarezHernandez

    Description

    Using Ollama you can easily run different LLM models in your local computer. This project is about exploring Ollama, testing different LLMs and try to fine tune them. Also, explore potential ways of integration with Uyuni.

    Goals

    • Explore Ollama
    • Test different models
    • Fine tuning
    • Explore possible integration in Uyuni

    Resources

    • https://ollama.com/
    • https://huggingface.co/
    • https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/


    Automated Test Report reviewer by oscar-barrios

    Description

    In SUMA/Uyuni team we spend a lot of time reviewing test reports, analyzing each of the test cases failing, checking if the test is a flaky test, checking logs, etc.

    Goals

    Speed up the review by automating some parts through AI, in a way that we can consume some summary of that report that could be meaningful for the reviewer.

    Resources

    No idea about the resources yet, but we will make use of:

    • HTML/JSON Report (text + screenshots)
    • The Test Suite Status GithHub board (via API)
    • The environment tested (via SSH)
    • The test framework code (via files)


    ghostwrAIter - a local AI assisted tool for helping with support cases by paolodepa

    Description

    This project is meant to fight the loneliness of the support team members, providing them an AI assistant (hopefully) capable of scraping supportconfigs in a RAG fashion, trying to answer specific questions.

    Goals

    • Setup an Ollama backend, spinning one (or more??) code-focused LLMs selected by license, performance and quality of the results between:
    • Setup a Web UI for it, choosing an easily extensible and customizable option between:
    • Extend the solution in order to be able to:
      • Add ZIU/Concord shared folders to its RAG context
      • Add BZ cases, splitted in comments to its RAG context
        • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query BZ
      • Add specific packages picking them from IBS repos
        • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query IBS
        • A plus would be to desume the packages of interest and the right channel and version to be picked from the added BZ cases


    Save pytorch models in OCI registries by jguilhermevanz

    Description

    A prerequisite for running applications in a cloud environment is the presence of a container registry. Another common scenario is users performing machine learning workloads in such environments. However, these types of workloads require dedicated infrastructure to run properly. We can leverage these two facts to help users save resources by storing their machine learning models in OCI registries, similar to how we handle some WebAssembly modules. This approach will save users the resources typically required for a machine learning model repository for the applications they need to run.

    Goals

    Allow PyTorch users to save and load machine learning models in OCI registries.

    Resources


    Make more sense of openQA test results using AI by livdywan

    Description

    AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.

    User Story

    Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?

    Goals

    • Leverage a chat interface to help Allison
    • Create a model from scratch based on data from openQA
    • Proof of concept for automated analysis of openQA test results

    Bonus

    • Use AI to suggest solutions to merge conflicts
      • This would need a merge conflict editor that can suggest solving the conflict
    • Use image recognition for needles

    Resources

    Timeline

    Day 1

    • Conversing with open-webui to teach me how to create a model based on openQA test results

    Day 2

    Highlights

    • I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
    • Convincing the chat interface to produce code specific to my use case required very explicit instructions.
    • Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
    • Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses

    Outcomes

    • Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
    • Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.


    FamilyTrip Planner: A Personalized Travel Planning Platform for Families by pherranz

    Description

    FamilyTrip Planner is an innovative travel planning application designed to optimize travel experiences for families with children. By integrating APIs for flights, accommodations, and local activities, the app generates complete itineraries tailored to each family’s unique interests and needs. Recommendations are based on customizable parameters such as destination, trip duration, children’s ages, and personal preferences. FamilyTrip Planner not only simplifies the travel planning process but also offers a comprehensive, personalized experience for families.

    Goals

    This project aims to: - Create a user-friendly platform that assists families in planning complete trips, from flight and accommodation options to recommended family-friendly activities. - Provide intelligent, personalized travel itineraries using artificial intelligence to enhance travel enjoyment and minimize time and cost. - Serve as an educational project for exploring Go programming and artificial intelligence, with the goal of building proficiency in both.

    Resources

    To develop FamilyTrip Planner, the project will leverage: - APIs such as Skyscanner, Google Places, and TripAdvisor to source real-time information on flights, accommodations, and activities. - Go programming language to manage data integration, API connections, and backend development. - Basic machine learning libraries to implement AI-driven itinerary suggestions tailored to family needs and preferences.


    ghostwrAIter - a local AI assisted tool for helping with support cases by paolodepa

    Description

    This project is meant to fight the loneliness of the support team members, providing them an AI assistant (hopefully) capable of scraping supportconfigs in a RAG fashion, trying to answer specific questions.

    Goals

    • Setup an Ollama backend, spinning one (or more??) code-focused LLMs selected by license, performance and quality of the results between:
    • Setup a Web UI for it, choosing an easily extensible and customizable option between:
    • Extend the solution in order to be able to:
      • Add ZIU/Concord shared folders to its RAG context
      • Add BZ cases, splitted in comments to its RAG context
        • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query BZ
      • Add specific packages picking them from IBS repos
        • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query IBS
        • A plus would be to desume the packages of interest and the right channel and version to be picked from the added BZ cases