[R] PCA rotation makes non-Matryoshka embeddings truncatable — 27x compression at 99% recall with reranking

Most embedding models (BGE-M3, E5, ada-002, Cohere) weren't trained with Matryoshka losses, so you can't just drop trailing dimensions. We tried: truncating BGE-M3 from 1024 to 256 dims gives 0.467 cosine similarity. Unusable. The fix is embarrassingly simple. Fit PCA on a sample of your embeddings