we were lucky to discover the new gte-small[0] model while writing this, which is so new that it doesn't have a lot of documentation around it. The neat thing about this model is that it out-performs OpenAI's `text-embedding-ada-002` on Hugging Face's MTEB Leaderboard[1] and it's only 384 dimensions. This is great for Postgres & pgvector - it means less space and faster queries.
There are some trade-offs (it was trained on English-only text), but this is a good indicator of the direction where embeddings might go (code-specific, language-specific, etc)
There are some trade-offs (it was trained on English-only text), but this is a good indicator of the direction where embeddings might go (code-specific, language-specific, etc)
[0] https://huggingface.co/Supabase/gte-small
[1] MTEB leaderboard: https://huggingface.co/spaces/mteb/leaderboard