1. The Dilemma of Traditional RAG2. Innovative Breakthroughs of Agentic RAG3. Advantages and Application Prospects of Agentic RAG In the context of rapid development in artificial intelligence, significant progress has been made in large language model (LLM) technology, but it also faces many challenges. Retrieval-Augmented Generation (RAG) technology has emerged to provide new avenues for … Read more
RAG (Retrieval-Augmented Generation) and Agentic RAG primarily differ in their functional scope and execution methods. Here is a detailed comparison: 1. RAG (Retrieval-Augmented Generation): Combines retrieval and generation. The system retrieves relevant information from external knowledge bases and uses generative models (like GPT) to generate answers based on the retrieval results. ① Passivity: Generates answers … Read more
Paper link: https://arxiv.org/abs/2501.09136 Github repository: https://github.com/asinghcsu/AgenticRAG-Survey Many links below are in this Github repository, and you can access more information by visiting the Github repository. Abstract Agentic Retrieval-Augmented Generation (Agentic RAG) represents a significant leap in the field of artificial intelligence by embedding autonomous agents within the RAG pipeline.This repository supplements the review paper “Agentic … Read more
The RAG technology will not disappear in 2025; instead, it will be more widely and deeply applied with the new paradigm Agentic RAG. Here is the fresh overview of the full-stack technology of Agentic RAG, just released in 2025: A comprehensive review of the development history of RAG, from the initial Naïve RAG to Advanced … Read more
Hello everyone, I am Chen Ge! If you are a developer or an AI enthusiast, you might want to quickly extract answers from a large volume of documents without flipping through every page. Today, I will guide you on how to build a localized Retrieval-Augmented Generation (RAG) system to get precise answers directly from documents. … Read more
Source: Knowledge Graph Technology This article has 2500 words and is recommended to read in 5 minutes. This article will help you understand how knowledge graphs can improve Retrieval-Augmented Generation (RAG) for information retrieval within companies. In this part of our AI series for knowledge management, you will learn how knowledge graphs enhance Retrieval-Augmented Generation … Read more
Source: DeepHub IMBA This article is approximately 2600 words and suggests a 5-minute reading time. This article will attempt to provide recommendations for choosing the correct generative AI methods based on some common quantifiable metrics. Generative AI is rapidly evolving, and many people are trying to use this technology to solve their business problems. Generally, … Read more
Source: DeepHub IMBA This article is approximately 1800 words and is suggested to be read in 6 minutes. This article will introduce how to integrate and create a scalable and customizable agent RAG. Building large language model applications can be quite challenging, especially when we have to choose between different frameworks like LangChain and LlamaIndex. … Read more
Photographer: Product Manager Fried Crab Shell When it comes to RAG or Agent, many people immediately think of LangChain or LlamaIndex, as they seem to believe these two are standard tools for developing applications with large models. But for me, I particularly dislike these two. Because they are the typical representatives of over-encapsulation. Especially with … Read more
Lu Xingguo Ye Xiangjun (2)T−B− SCID 1. Recombinase-activating Genes1 and 2 Deficiency Recombinase-activating genes1 and 2 (recombinase-activating genes 1 and 2，RAG1/2) deficiency accounts for approximately6% of SCID patients. In about75% of patients with RAG1/2 deficiency, there are very low numbers of T, B lymphocytes, and NK cells. This type of SCID is caused by mutations … Read more