Detailed steps and code are provided for how to choose the best embedding model and reranker model. When building a Retrieval-Augmented Generation (RAG) pipeline, one of the key components is the retriever. We have various embedding models to choose from, including OpenAI, CohereAI, and open-source sentence transformers. Additionally, there are several rerankers available from CohereAI … Read more
Is there a difference between LLM large models and embedding large models in the knowledge base domain? Why is it necessary to set up a separate embedding large model in the RAG field? In the field of artificial intelligence, large language models (LLMs) and embedding models are two key technologies in natural language processing (NLP), … Read more
Retrieval-Augmented Generation (RAG) is a technology that combines retrieval and generation, effectively addressing some issues of large language models (LLMs), such as hallucinations and knowledge limitations. With the development of RAG technology, vector technologies involved in RAG have gained attention, and vector databases are gradually being understood by everyone. Some established database vendors have also … Read more
Reported by Machine Heart Editor: Chen Ping It seems that paper search tools are starting to become competitive! For those who search for papers every day, having a good search tool is simply delightful, and efficiency definitely increases. But the reality is that either the search tool is ineffective, or the keywords you input are … Read more
The RAG (Retrieval-Augmented Generation) model, commonly referred to as the RAG system, is widely used in large model applications. The principle of the model is quite simple: it retrieves information from a dataset based on user needs and then uses a large model for reasoning and generation. The advantage of RAG lies in its ability … Read more