1. You make a great point about longer documents requiring multiple vectors which I should've mentioned in the post. Depending on your use case, this can certainly explode your dataset size!
2. Good to know about the pgvector limitations -- I haven't used it yet.
3. I guess "index" would be the more database-y term. That said, one thing I'll call out is that you have to re-index if you ever change your embedding model, and indexing can be slow. It took me ~20-30 minutes to index the 10 million embeddings in my benchmark.
1. You make a great point about longer documents requiring multiple vectors which I should've mentioned in the post. Depending on your use case, this can certainly explode your dataset size! 2. Good to know about the pgvector limitations -- I haven't used it yet. 3. I guess "index" would be the more database-y term. That said, one thing I'll call out is that you have to re-index if you ever change your embedding model, and indexing can be slow. It took me ~20-30 minutes to index the 10 million embeddings in my benchmark.