ghostwrAIter - a local AI assisted tool for helping with support cases
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
-
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
"what is it" file and directory analysis via MCP and local LLM, for console and KDE by rsimai
Description
Users sometimes wonder what files or directories they find on their local PC are good for. If they can't determine from the filename or metadata, there should an easy way to quickly analyze the content and at least guess the meaning. An LLM could help with that, through the use of a filesystem MCP and to-text-converters for typical file types. Ideally this is integrated into the desktop environment but works as well from a console. All data is processed locally or "on premise", no artifacts remain or leave the system.
Goals
- The user can run a command from the console, to check on a file or directory
- The filemanager contains the "analyze" feature within the context menu
- The local LLM could serve for other use cases where privacy matters
TBD
- Find or write capable one-shot and interactive MCP client
- Find or write simple+secure file access MCP server
- Create local LLM service with appropriate footprint, containerized
- Shell command with options
- KDE integration (Dolphin)
- Package
- Document
Resources
Docs Navigator MCP: SUSE Edition by mackenzie.techdocs
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
- Repository: Docs Navigator MCP: SUSE Edition GitHub
- UI Demo: Live UI Demo of Docs Navigator MCP: SUSE Edition
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
Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios
Description
Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.
This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.
The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.
Goals
By the end of Hack Week, we aim to have a single, working Python script that:
- Connects to Prometheus and executes a query to fetch detailed test failure history.
- Processes the raw data into a format suitable for the Gemini API.
- Successfully calls the Gemini API with the data and a clear prompt.
- Parses the AI's response to extract a simple list of flaky tests.
- Saves the list to a JSON file that can be displayed in Grafana.
- New panel in our Dashboard listing the Flaky tests
Resources
- Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
- Data Source: Our internal Prometheus server.
- Key Metric:
jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}. - Existing Query for Reference:
count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range])). - AI Model: The Google Gemini API.
- Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
- Visualization: Our internal Grafana Dashboard.
- Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring
Outcome
- Jenkins Flaky Test Detector: https://github.com/srbarrios/jenkins-flaky-tests-detector and its container
- IaC on MLM Team: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring/jenkinsflakytestsdetector?reftype=heads, https://gitlab.suse.de/galaxy/infrastructure/-/blob/master/srv/salt/monitoring/grafana/dashboards/flaky-tests.json?ref_type=heads, and others.
- Grafana Dashboard: https://grafana.mgr.suse.de/d/flaky-tests/flaky-tests-detection @ @ text
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
Description
Project Achievements during Hackweek
In this file you can read about what we achieved during Hackweek.