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Has anybody in my network set up local notes (Markdown files) with an Ollama RAG/model trained on such notes? So far, below is the best shot I found. I want to play around over Christmas break and wonder about the best approaches. I collect some ideas here: www.ssp.sh/brain/second.... #obsidian

Have you looked at github.com/your-papa/ob...? It looks most what I want right now. But I'm unsure about my Zettelkasten as the note says: "Quality depends on knowledge structure and organization? I'm in the same boat as Joseph, I want a local model/RAG trained on my 7k notes.

Do you want an Obsidian Integration or how you want to trigger it? I set one up locally pulling in from Notion, with a custom frontend, and a Lance vectordb in the backend.

I don't mind the interface; it can be a CLI or a script. No need for a UI, the content and quality of responses matter more. How did your backend "learn" or connect based on your notion pulled data? Does Lance have a custom search/algorithm you use? Or do you add additional steps?

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I use hybrid search + a reranker on top of Lance. Did some light relevant engineering and do a weighting based on the different fields (e.g. title, body, headings in the page). I did not do any query understanding because I know how to write my queries to have a good retrieval score.

For me the responses aren't the most important thing, but rather the bare results. I needed a better search to find my stuff. I would say the responses are all over the place, because my Notion often is very deep in a topic, where the smaller models just don't have any relevant knowledge

I wonder, for my use-case, if I would rather focus on the response just to highlight the most relevant note or connection of notes. I would have more context in that related note, written by myself, instead of expecting new insights based on my notes. Or maybe both 🤔😅