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
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
This article is about 11,000 words long and is recommended to be read in 6 minutes. This article introduces large models + RAG. 1 Introduction Large Language Models (LLMs) have limitations when handling domain-specific or highly specialized queries, such as generating inaccurate information or “hallucinations.” A promising approach to mitigate these limitations is Retrieval-Augmented Generation … Read more
Datawhale Insights Author: Song Zhixue, Datawhale Member Hello everyone, I am not a fan of garlic and ginger. Next, I will guide you step by step to implement a simple RAG model, which is a simplified version of RAG, called Tiny-RAG. Tiny-RAG is a simplified version of RAG that only includes the core functions of … Read more
Introduction Hello everyone, I am Liu Cong from NLP. RAG (Retrieval-Augmented Generation) finds information fragments relevant to user questions through a retrieval system, utilizing large models to synthesize an answer. This greatly addresses issues such as hallucination and outdated information in large models, and has become an important means for the practical application of large … Read more
As one of the most advanced technologies in artificial intelligence, Retrieval-Augmented Generation (RAG) technology can provide reliable and up-to-date external knowledge, offering great convenience for numerous tasks. Especially in the era of AI-Generated Content (AIGC), RAG’s powerful retrieval capabilities in providing additional knowledge enable it to assist existing generative AI in producing high-quality outputs. Recently, … Read more
Original: https://zhuanlan.zhihu.com/p/682253496 Compiled by: Qingke AI Leave a message in the backend ‘ Exchange ‘, Join the NewBee discussion group 1. Background Introduction RAG (Retrieval Augmented Generation) method refers to a combination of retrieval-based models and generative models to improve the quality and relevance of generated text. This method was proposed by Meta in the … Read more
Original: https://zhuanlan.zhihu.com/p/682253496 Organizer: Qingke AI 1. Background Introduction The RAG (Retrieval Augmented Generation) method refers to a combination of retrieval-based models and generative models to improve the quality and relevance of generated text. This method was proposed by Meta in the 2020 paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”[1], allowing language models (LM) to acquire … Read more
Introduction This article provides an overall introduction to the LlamaIndex framework, including its functions, components, and explanations. LlamaIndex is an open-source LLM application development framework built on large models (including Agents and Workflows) to create context-enhanced generative AI applications. Components of LlamaIndex The following diagram illustrates the overall functional structure of LlamaIndex, abstracted from the … Read more
Click the bottom “Read Original” to browse the detailed content of “CCF Digital Focus” Issue 48 Editor’s Note Large language models (LLMs) still face many challenges when dealing with domain-specific or knowledge-intensive tasks, such as generating hallucinations, outdated knowledge, and opaque, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) technology has emerged to address these issues. RAG … Read more