RAG Series (5): Embedding Models — The Core of Semantic Understanding

Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding . It transforms human-readable text into machine-computable vectors. The choice of Embedding model directly determines: Whether "apple" and "iPhone" are recognized as related Whether "d