Embedding Explained: How Vectorization Turns Text into Numbers for RAG
This article walks through why traditional keyword matching fails for RAG, explains the evolution from one‑hot encoding to Word2Vec and BERT, details sentence‑level embeddings and similarity metrics, compares leading Chinese and multilingual embedding models using the C‑MTEB benchmark, and provides practical LangChain code, deployment tips, and common pitfalls.
