An OSS clone of perplexity.ai / phind.com with convex!
Hello everyone 👋,
I used Convex as the backend during a hackathon held at AGI House last Saturday! It has been built with the following stack:
- Utilizes Next.js 14.
- Hosted by Vercel.
- Backend and vector embedding-powered caching are on Convex.
- Google Search is powered by serper.dev.
The demo can be found at https://bobtail.dev, and the code is available at https://github.com/wsxiaoys/bobtail.dev.
It should provide a comprehensive end-to-end overview of how an LLM + RAG system shall be built using Convex. Enjoy!
4 Replies
Hi @mzh welcome to the community and thanks for sharing!
Wow this is really impressive. nice work @mzh. Would you want it considered for a template on convex.dev/templates ? We have a few AI rag chat examples but not a web-powered rag search engine, and you did a fantastic job with this one. Very clean code, especially given it was a hackathon.
One question - the cron to calculate embeddings for searches - why not do that in
rag after doing updateContent ? At that point it won't be interfering with user perceived latency, and that way it'd be updated right away, instead of later on. Btw getSearchesWithoutQueryEmbeddings uses a filter which scans rows, so will fail after ~10k documents searches are in the table. You could use an index, but if you just write the embedding immediately I think this function can go away.Would you want it considered for a template on convex.dev/templatesTo make sure it's suitable as a scaffold, it would probably be beneficial to refactor the front end to utilize shadcn/ui for its simplicity and minimalistic aesthetic, along with employing a copy-paste approach for quick modifications. Although I might not be able to undertake this task in the near future, I will keep it in mind and consider it as an opportunity to learn more about shadcn/ui + tailwind
One question - the cron to calculate embeddings for searches - why not do that in rag after doing updateContent ? At that point it won't be interfering with user perceived latency, and that way it'd be updated right away, instead of later on. Btw getSearchesWithoutQueryEmbeddings uses a filter which scans rows, so will fail after ~10k documents searches are in the table. You could use an index, but if you just write the embedding immediately I think this function can go away.It was added as back-filling logic during the hackathon. Right it would be cleaner to simply schedule the embedding when creating the query.
@mzh I just tried this after a while and it's erroring out - is this project still live?