quivr VS supabase

3. Quivr | GitHub | tutorial

I used Ollama (with Mistral 7B) and Quivr to get a local RAG up and running and it works fine, but was surprised to find there are no easy user-friendly ways to do it. Most other local LLM UIs don't implement this use case (I looked here), even though it is one of the most useful local LLM use-cases I can think of: search and summarize information from sensitive / confidential documents.

In my opinion LLM based document search tools such as OSS Quivr may be better suited for documentation search for startups.

A highly customed Quivr with one of the 'Open Source LLMs' may provides great 'semantic search' for product documentation.

https://github.com/StanGirard/quivr

Hey thanks for creating this, will try later if i have time. Meanwhile, do you try some of other second brain app such as this, and how was the comparison? The one i mentioned was trending on github so i think its decent (been playing with it since last week or so, also). But i already starred your repo so i can come back later.

It was a great experience using Supabase’s rock-solid PostgreSQL database for this app. The DX around that product is phenomenal: viewing and managing the DB data was a lifesaver when you don’t want to craft your own admin panel from scratch.

I didn't really give much thought as to which backend I would use. I already had 2 projects in Supabase (BOXCUT & MineWork), but also a few projects in Firebase too. I was more concerned at the time at actually building the product.

Sign up for SupaBase: Head over to SupaBase and sign up. Create a new workspace and project with your preferred names.

Setting up Supabase Create a new Supabase project, and get the connection string for the database from settings > database.

Supabase (start for free)

Built with Supabase, Astro, Unreal Speech, Stable Diffusion, Replicate, Metropolitan Museum of Art

Supabase will be used for storing article data in the database and the cover image of the article in storage. Chakra UI will be used to provide style to the elements. By using both, we can build the blog with ease.

For AutoRepurpose, I opted for Supabase as the backbone of the backend. It has reliably supported Penelope AI, which garnered over 15k users in 2022 without any issues.

Semantic search demo

1. Create a new Supabase project: Visit Supabase and create a new project.