Article Summary:
Building an AI-powered editorial assistant for Fedora Community Blog and Fedora Magazine using RamaLama RAG — a tool that helps new authors check whether their draft articles meet editorial standards before submission.
Article Description:
As part of my Outreachy internship with the Fedora Project and under the mentorship of Justin Wheeler, Carol Chen, and Dominik Kwakwa, I built a Retrieval-Augmented Generation (RAG) pipeline using RamaLama, enhanced on the Fedora Community Blog and Fedora Magazine writing guidelines, and past published articles. The result is an editorial assistant that reviewers and new authors can use to check draft articles against Fedora’s editorial standards — flagging missing required elements, such as featured images and Read More tags, checking tone, structure, technical accuracy, and topic scope.
The article would cover:
- The motivation behind the project: making Fedora’s editorial knowledge accessible to new contributors
- How the pipeline works: fetching articles via the WordPress REST API, processing them with Docling, building a vector store with RamaLama RAG, and querying it with open-weight models (Qwen, Gemma, Granite)
- What I learned: the impact of chunking strategy on retrieval quality, why corpus quality matters as much as model choice, and what it means to run this entirely locally on open-source tooling
- A working Streamlit GUI that lets authors paste a draft and get instant editorial feedback
- How others in the Fedora community could use or extend the tool.
The project is fully open source under Apache 2.0:
We, my mentors and I, believe this would be useful to Fedora CommBlog and Magazine readers who are contributors, authors, or editors — and it directly demonstrates the potential of local, open-weight AI tooling within the Fedora ecosystem.
I have read and understand the Ai-Assisted Contributions Policy
Assisted-by: Claude by Anthropic
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