MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, targeting 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 circles of natural language processing and machine learning, especially for beginners. … Read more
Author: Raunak Jain March 18, 2024 Translator: Chen Zhiyan Proofreader: zrx This article is approximately 3500 words long and is suggested to be read in 10 minutes. How to build a flow-configurable compound artificial intelligence system using open-source tools. In the previous section, we introduced what a compound artificial intelligence system is, its system components, … Read more
Introduction RAG has developed to become a consensus for LLM’s service to B-end, however, questions regarding it have never ceased to exist. Simply put: for many Q&A systems represented by individuals and small to medium enterprises, there is indeed no need to use RAG. However, these long-context LLMs have either already addressed or are in … Read more
1 Algorithm Introduction Retrieval Augmented Generation (RAG) has become one of the hottest applications of large language models (LLM). After the recent boom in large models, everyone must have a certain understanding of their capabilities. However, when we apply large models to practical business scenarios, we find that generic foundational models generally cannot meet our … Read more
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 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
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: Deep Graph Learning and Large Model LLM This article is approximately 3500 words long and is recommended for a 9-minute read. It delves into the development of Retrieval-Augmented Generation (RAG), from basic concepts to the latest technologies. 4. Overview of Existing RAG Frameworks Agent-Based RAG A new agent-based Retrieval-Augmented Generation (RAG) framework employs a … Read more