Author: Kuang Ji Reviewed by: Los Introduction: The author provides the most important design guidelines for RAG system design at various stages. This analysis covers five stages: indexing, storage, retrieval, synthesis, and evaluation, detailing the important design rules for each stage of the RAG system. The author combines practical experience in building RAG systems with … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university professors, and researchers from enterprises. The vision of the community is to promote communication and progress between the academic and industrial worlds of natural language processing and machine learning, especially for the … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university professors, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for the advancement … Read more
Have you ever been troubled by these questions? Searching through a cluttered computer hard drive for an assignment you saved, clearly remembering the general content, but unable to find it by name because the file name has little to do with the content; or when writing a paper, you remember a piece of literature supporting … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, with an audience covering NLP graduate students, university teachers, and industry researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for the … Read more
Author: Luv Bansal Translation: wwl Proofreading: Zhang Yiran This article is approximately 4400 words long and is recommended for a reading time of over 10 minutes. This article discusses various techniques for optimizing different parts of the RAG pipeline and enhancing the overall RAG workflow. Image generated by the author using Dalle-3 provided by Bing … Read more
Originally published on Data Analysis and Applications 1 Introduction This article provides a comprehensive review of the current state-of-the-art RAG technology, including Naive RAG, Advanced RAG, and Modular RAG paradigms, all within the context of LLMs. The article discusses core technologies in the RAG process such as “retrieval,” “generation,” and “enhancement,” and delves into their … Read more
Source: NewBeeNLP This article is about 3300 words long, and it is recommended to read for 6 minutes. This article introduces Retrieval-Augmented Generation (RAG), a powerful technology that can significantly enhance the performance of large language models. Retrieval-Augmented Generation (RAG) is a powerful technology that can significantly enhance the performance of large language models. The … Read more
MLNLP community is a well-known machine learning and natural language processing community at home and abroad, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning at home and abroad, … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted … Read more