Enterprises build large model RAG knowledge base? Which Embedding model should I choose?

Written by

Audrey Miles

Updated on:July-11th-2025

Requirements: When enterprises build RAG knowledge bases, it is important to choose a suitable embedding model. The performance of embedding determines the accuracy of retrieval and indirectly determines the credibility of the output of the large model. Common models: bge, m3e, nomic-embed-text, BCEmbedding (NetEase Youdao). Why do we need an embedding model? Computers can only process digital operations in nature and cannot directly understand non-numerical data such as natural language, text, pictures, and audio. Therefore, we need to use "vectorization" operations to convert these data into numerical forms that computers can understand and process, that is, map them into mathematical vector representations. This process is usually achieved with the help of an embedding model, which can effectively capture the semantic information and intrinsic structure in the data. The role of the embedding model is that it can not only convert discrete data (such as words, image fragments, or audio fragments) into continuous low-dimensional vectors, but also retain the semantic relationship between data in the vector space. For example, in natural language processing, the embedding model can generate word vectors so that semantically similar words are closer in the vector space. This efficient representation enables computers to perform complex calculations and analyses based on these vectors, thereby better understanding and processing complex data such as text, images, or sounds. Through the vectorization operation of the embedding model, computers can not only process large-scale data efficiently, but also demonstrate stronger performance and generalization capabilities in various tasks (such as classification, retrieval, generation, etc.). Embedding model evaluation To judge the quality of an embedding model, there must be a set of clear standards. MTEB and C-MTEB are usually used for benchmarking. MTEB Huggingface has an MTEB (Massive Multilingual Text Embedding Benchmark) evaluation standard, which is a relatively recognized standard in the industry and can be used as a reference. It covers 8 embedding tasks, a total of 58 data sets and 112 languages. It is the most comprehensive text embedding benchmark to date.