From Word2Vec to LLM2Vec: How to Choose the Right Embedding Model for RAG

This post provides a comprehensive guide on evaluating embedding models for RAG systems, detailing eight key factors: context window, tokenization unit, dimensionality, vocabulary size, training data, cost, MTEB score, and domain specificity. It also discusses additional perspectives like multilingual alignment and adversarial testing. While not directly adding new Milvus features, it significantly enhances the understanding of how to best leverage Milvus within a RAG architecture by guiding users on selecting optimal embedding models, which directly impacts the effectiveness of Milvus's vector search capabilities in RAG applications.