How to choose an embedding model

3-Second Blueprint (English)

1. Embedding models map text or multimodal data into high-dimensional vector spaces where cosine similarity measures semantic closeness.
2. Selecting a model balances general vs domain-specific training, task tuning (retrieval, reranking), parameter size, and latency.
3. Matryoshka Representation Learning compresses embeddings, while MTEB benchmarks and custom dataset tests validate accuracy.