Hi there! When Supabase announced their recent hackathon, I thought it was a good time to build something to learn more about so many of the new AI models and tech out there. From the different techniques of embedding documents to the future RAG.
With the rise of short form content with TikTok and Youtube. A lot more knowledge is in videos than ever before. Finding specific answers within millions of videos can be difficult for any one person to go through. So the question is if there is Google that indexes text on website making it easier to find based on the context of on your question, why is there no Google that indexes video content making it easier for users to find answers within them.
So I built this to showcase that it's very much possible with the technology and infrastructure that is readily available.
I've indexed thousands of videos from Youtube and will be adding more, some of the things coming soon:
- Index TikTok videos
- Using whisper to transcribe audio in videos that don't have captions
- Auto scraping both Youtube and Tiktok everyday to add new content
The tech stack:
- Supbase (PostgreSQL, PG_Vector, Auth)
- Hasura (GraphQL layer, permissions)
- Fly (Hosting of Hasura)
- JigsawStack (Summary AI, Chat AI)
- Vercel (NextJS hosting, Serverless functions)
The code is opensource here:https://github.com/yoeven/ai-video-search-engine
Would love some feedback and thoughts on what you would like to see?
It's effectively just a very simple "indexer", if you can even really call it that because it's only chunking audio for semantic search rather than actually indexing it.
Search engines are hard because of ranking and scale. This project does not solve either of those problems.
Personally if I was going to build this I would exclusively focus on the data side and use a pre-built traditional search engine like meilisearch, typesense, elasticsearch, etc to handle the indexing and search side. Adding semantic search into the mix upfront makes your life so much harder.