ghostwrAIter - a local AI assisted tool for helping with support cases
an invention by paolodepa
Updated 12 months 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

  • 12 months 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
      12 months 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

    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 Python web server (e.g., using FastAPI) 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

    Uyuni Health-check Grafana Troubleshooter by ygutierrez

    Description

    This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.

    Goals

    • Investigate if and how the grafana-llm-app plug-in can be used within the Uyuni Health-check tool.
    • Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
    • Evaluate support for local LLMs and external APIs through the plugin.

    Resources

    Grafana LMM plug-in

    Uyuni Health-check

    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

    https://docs.google.com/document/d/1HbAvgrg8T3pd1FIx74nEfCObCljpO77zz5In_Jpw4as/edit?usp=sharing## Description

    MCP Server for SCC by digitaltomm

    Description

    Provide an MCP Server implementation for scc.suse.com. Similar to the organization APIs, this can expose to customers data about their subscriptions, orders, systems and products. Authentication should be done by organization credentials, similar to what needs to be provided to RMT/MLM.

    Goals

    We want to show a proof of concept to connect to the SCC MCP server with any agentic AI, like gemini-cli, copilot or Claude desktop. This would enable the user to ask questions regarding their SCC inventory, like "When do I need to re-new my SLES subscription", "Do I have active systems running on unsupported operating systems?". The user can decide themselves to use any AI backend which they trust to use for this.

    Resources

    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.