Starting with embeddings, we hope to simplify and improve the developer experience when working with embeddings. Supabase already has great support for storage and retrieval of embeddings (thanks to pgvector) [0], so it feels like this collaboration was long overdue!
Open-source embedding models are both smaller and more performant [1] than closed-source alternatives, so it's quite surprising that 98% of Supabase applications currently use OpenAI's text-embedding-ada-002 [2]. Probably because it is just easier to access? Well... that changes today! You can also iterate extremely quickly: experiment with and choose the model that works best for you (no vendor lock-in)! In fact, since the article was written, a new leader has just appeared on top of the MTEB leaderboard [3].
I look forward to answering any questions you have!
[0] https://supabase.com/vector [1] https://huggingface.co/spaces/mteb/leaderboard [2] https://supabase.com/blog/hugging-face-supabase [3] https://huggingface.co/BAAI/bge-large-en