RAG 75% for retrieval, 25% for generation. All aimed at enhancing the ability to retrieve relevant contextual information. 1. RAG Working Principle and Technical Architecture RAG (Retrieval-Augmented Generation) is a technical architecture that combines information retrieval and generation models. Its basic working principle is to enhance the output of the generation model through the retrieval … Read more
LLM (Large Language Model) is a powerful new platform, but they are not always trained on data relevant to our tasks or the latest data. RAG (Retrieval Augmented Generation) is a general method that connects LLMs with external data sources (such as private or up-to-date data). It allows LLMs to use external data to generate … Read more
Introduction With the development of large models, vector databases have also ushered in broader application prospects. They not only assist large models in inference acceleration but also save costs for users. Moreover, vector databases are an effective means to address the limitations of large models. As a result, more and more large model deployment projects … Read more