I made something similar in my project. My more difficult task has been choice the right approach to chunking long documents. I used both structural and semantic chunking approach. The semantic one helped to better store vectors in vectorial DB. I used QDrant and openAi embedding model.

What was the system prompt?

What would it look like to regularly react to source data changes? Seems like a big missing piece. Event based? regular cadence? Curious what people choose. Great post though.

This article came just in the nick of time. I'm in fandoms that lean heavily into fanfiction, and there's a LOT out there on Ao3. Ao3 has the worst search (and yo can't even search your account's history!), so I've been wanting to create something like this as a tool for the fandom, where we can query "what was the fic about XYZ where ABC happened?" and get hopefully helpful responses. I'm very tired of not being able to do this, and it would be a fun learning experience.

I've already got the data mostly structured because I did some research on the fandom last year, charting trends and such, so I don't even need to massage the data. I've got authors, dates, chapters, reader comments, and full text already in a local SQLite db.

Cool to see Nomic embeddings mentioned. Though surpriser you didn't land on Voyage.

Did you look at Turbopuffer btw?

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