I did say the index, not the embeddings themselves. The index is a more compact representation of your embeddings collection, and that's what you need in memory. One approach for indexing is to calculate centroids of your embeddings.

You have multiple parameters to tweak, that affect retrieval performance as well as the memory footprint of your indexes. Here's a rundown on that: https://tembo.io/blog/vector-indexes-in-pgvector