ghostwrAIter - a local AI assisted tool for helping with support cases
an invention by paolodepa
Updated about 1 year ago. 4 hacker ♥️. 1 follower.

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

Looking for hackers with the skills:

ai support

This project is part of:

Hack Week 24

Activity

  • about 1 year ago: paolodepa started this project.
  • about 1 year ago: m.crivellari liked this project.
  • about 1 year ago: HvdHeuvel liked this project.
  • about 1 year ago: livdywan liked this project.
  • about 1 year ago: lthadeus liked 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 originated this project.


    • Comments

    • paolodepa
      about 1 year ago by paolodepa | Reply

      The project soon moved to CLI, as the skills for integrating a WEB-UI are not my cup of tea :-/
      Its description and source code can be found at ghostwrAIter

      I tested the listed LLMs and also the following embedding models: mxbai-embed-large, nomic-embed-text, all-minilm.
      My impression is that the current state of the art for the really open-source llms and embedding models is not still mature and ready for production grade and that a big gap exists with the most well-known commercial product.

      Hopefully will run a refresh for the next hackweek.

    Similar Projects

    Kubernetes-Based ML Lifecycle Automation by lmiranda

    Description

    This project aims to build a complete end-to-end Machine Learning pipeline running entirely on Kubernetes, using Go, and containerized ML components.

    The pipeline will automate the lifecycle of a machine learning model, including:

    • Data ingestion/collection
    • Model training as a Kubernetes Job
    • Model artifact storage in an S3-compatible registry (e.g. Minio)
    • A Go-based deployment controller that automatically deploys new model versions to Kubernetes using Rancher
    • A lightweight inference service that loads and serves the latest model
    • Monitoring of model performance and service health through Prometheus/Grafana

    The outcome is a working prototype of an MLOps workflow that demonstrates how AI workloads can be trained, versioned, deployed, and monitored using the Kubernetes ecosystem.

    Goals

    By the end of Hack Week, the project should:

    1. Produce a fully functional ML pipeline running on Kubernetes with:

      • Data collection job
      • Training job container
      • Storage and versioning of trained models
      • Automated deployment of new model versions
      • Model inference API service
      • Basic monitoring dashboards

    2. Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.

    3. Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).

    4. Prepare a short demo explaining the end-to-end process and how new models flow through the system.

    Resources

    Project Repository

    Updates

    1. Training pipeline and datasets
    2. Inference Service py

    Try out Neovim Plugins supporting AI Providers by enavarro_suse

    Description

    Experiment with several Neovim plugins that integrate AI model providers such as Gemini and Ollama.

    Goals

    Evaluate how these plugins enhance the development workflow, how they differ in capabilities, and how smoothly they integrate into Neovim for day-to-day coding tasks.

    Resources

    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

    Docs Navigator MCP: SUSE Edition by mackenzie.techdocs

    MCP Docs Navigator: SUSE Edition

    Description

    Docs Navigator MCP: SUSE Edition is an AI-powered documentation navigator that makes finding information across SUSE, Rancher, K3s, and RKE2 documentation effortless. Built as a Model Context Protocol (MCP) server, it enables semantic search, intelligent Q&A, and documentation summarization using 100% open-source AI models (no API keys required!). The project also allows you to bring your own keys from Anthropic and Open AI for parallel processing.

    Goals

    • [ X ] Build functional MCP server with documentation tools
    • [ X ] Implement semantic search with vector embeddings
    • [ X ] Create user-friendly web interface
    • [ X ] Optimize indexing performance (parallel processing)
    • [ X ] Add SUSE branding and polish UX
    • [ X ] Stretch Goal: Add more documentation sources
    • [ X ] Stretch Goal: Implement document change detection for auto-updates

    Coming Soon!

    • Community Feedback: Test with real users and gather improvement suggestions

    Resources

    Multi-agent AI assistant for Linux troubleshooting by doreilly

    Description

    Explore multi-agent architecture as a way to avoid MCP context rot.

    Having one agent with many tools bloats the context with low-level details about tool descriptions, parameter schemas etc which hurts LLM performance. Instead have many specialised agents, each with just the tools it needs for its role. A top level supervisor agent takes the user prompt and delegates to appropriate sub-agents.

    Goals

    Create an AI assistant with some sub-agents that are specialists at troubleshooting Linux subsystems, e.g. systemd, selinux, firewalld etc. The agents can get information from the system by implementing their own tools with simple function calls, or use tools from MCP servers, e.g. a systemd-agent can use tools from systemd-mcp.

    Example prompts/responses:

    user$ the system seems slow
assistant$ process foo with pid 12345 is using 1000% cpu ...

user$ I can't connect to the apache webserver
assistant$ the firewall is blocking http ... you can open the port with firewall-cmd --add-port ...

    Resources

    Language Python. The Python ADK is more mature than Golang.

    https://google.github.io/adk-docs/

    https://github.com/djoreilly/linux-helper