Embedding models and their recommended settings for RAG and semantic search.
nomic-embed-text embeddings locally with Ollama
nomic-embed-text for a 100% local English RAG with Ollama: 768 dimensions, long…
BGE-M3 embeddings for RAG over long documents
BGE-M3 for RAG over long documents: 1024 dimensions, up to 8,192 tokens, 100+…
An embedding model turns a text into a list of numbers (a vector). Texts with similar meaning give close vectors: it is the basis of semantic search and RAG.