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

    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

    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

    AI-Powered Unit Test Automation for Agama by joseivanlopez

    The Agama project is a multi-language Linux installer that leverages the distinct strengths of several key technologies:

    • Rust: Used for the back-end services and the core HTTP API, providing performance and safety.
    • TypeScript (React/PatternFly): Powers the modern web user interface (UI), ensuring a consistent and responsive user experience.
    • Ruby: Integrates existing, robust YaST libraries (e.g., yast-storage-ng) to reuse established functionality.

    The Problem: Testing Overhead

    Developing and maintaining code across these three languages requires a significant, tedious effort in writing, reviewing, and updating unit tests for each component. This high cost of testing is a drain on developer resources and can slow down the project's evolution.

    The Solution: AI-Driven Automation

    This project aims to eliminate the manual overhead of unit testing by exploring and integrating AI-driven code generation tools. We will investigate how AI can:

    1. Automatically generate new unit tests as code is developed.
    2. Intelligently correct and update existing unit tests when the application code changes.

    By automating this crucial but monotonous task, we can free developers to focus on feature implementation and significantly improve the speed and maintainability of the Agama codebase.

    Goals

    • Proof of Concept: Successfully integrate and demonstrate an authorized AI tool (e.g., gemini-cli) to automatically generate unit tests.
    • Workflow Integration: Define and document a new unit test automation workflow that seamlessly integrates the selected AI tool into the existing Agama development pipeline.
    • Knowledge Sharing: Establish a set of best practices for using AI in code generation, sharing the learned expertise with the broader team.

    Contribution & Resources

    We are seeking contributors interested in AI-powered development and improving developer efficiency. Whether you have previous experience with code generation tools or are eager to learn, your participation is highly valuable.

    If you want to dive deep into AI for software quality, please reach out and join the effort!

    • Authorized AI Tools: Tools supported by SUSE (e.g., gemini-cli)
    • Focus Areas: Rust, TypeScript, and Ruby components within the Agama project.

    Interesting Links

    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/

    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