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:
- deepseek-coder-v2
- dolphin-mistral
- starcoder2
- (...others??)
- 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
This project is part of:
Hack Week 24
Activity
Comments
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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
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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
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Hackweek STEP
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Scope
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- Connect to a live SUSE Observability instance and authenticate (with API token)
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- 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.
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Resources
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- 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
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Motivation
What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?
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Description
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- Implement Bugzillla call to collect the
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Resources
What are Systemic Questions?
Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.
Gitlab Project
gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question
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Example:
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Example:
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Technical Overview
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Resources
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Description
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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
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