Detailed Explanation of RAG 2.0 Architecture The so-called RAG, short for Retrieval-Augmented Generation, combines retrieval and generation technologies to enhance the effectiveness of text generation tasks. Its working principle combines the advantages of retrieval models and generation models to address some challenges and issues in text generation. RAG 2.0, on the other hand, is an … Read more
Source: Deep Learning and Large Models LLM This article is approximately 3500 words long and is recommended for a 9-minute read. This article delves into the development of Retrieval-Augmented Generation (RAG), from basic concepts to the latest technologies. All subfields of LLM + ACL25/ICML25/NAACL25 submission groups -> Enter from here for all subfields and submission … Read more
Abstract The RAG system is gradually revolutionizing our understanding of AI-driven information processing. To fully leverage its potential, understanding its fundamental principles is crucial. This article aims to succinctly analyze the RAG system, hoping to provide insights and resonance for readers. What is the RAG System? In short, the RAG system integrates large language models … Read more
Click Follow us by clicking the blue text above The RAG system poses privacy leakage risks, and researchers from the University of Perugia, the University of Siena, and the University of Pisa have proposed a correlation-based attack framework that utilizes open-source language models and sentence encoders to adaptively explore hidden knowledge bases, efficiently extracting private … Read more
The combination of AI and smart healthcare is an inevitable trend for future development. In recent years, the National Health Commission has promoted smart healthcare and AI technologies, such as intelligent triage, pre-consultation, and diagnostic assistance, to improve the efficiency of medical services and the accuracy of diagnoses, enhancing the patient experience. Quanzhentong is a … Read more
Milvus is a vector database that has long focused on embedding-based vector search capabilities, providing high accuracy, high performance, and highly scalable semantic search functions for applications like RAG. With the advent of the large model era bringing various new application explorations, the community has re-recognized the benefits of combining traditional text-matching precise search with … Read more
In recent years, the technology of Retrieval-Augmented Generation (RAG) has gained significant attention in the field of artificial intelligence. However, as demands have become more complex, traditional RAG has shown limitations in handling multi-step reasoning and external tool calls. To address this, Agentic RAG has emerged as an upgraded version of RAG, showcasing more powerful … Read more
In this article, we will explore how Agentic RAG helps to address the limitations of traditional RAG. RAG Framework The RAG (Retrieval-Augmented Generation) framework operates in a specific sequence: Document -> Document Fragments -> Vector Database -> Fragment Retrieval (Top K) -> Large Language Model (LLM) However, this order encounters obstacles when handling certain types … Read more
Today is January 18, 2025, Saturday, Beijing, clear weather. Let’s continue discussing RAG. Recently, there has been some work on Agentic RAG, which integrates autonomous agents to overcome the limitations of traditional RAG systems that perform well in knowledge retrieval and generation but struggle with dynamic, multi-step reasoning tasks, adaptability, and complex workflow orchestration. So, … Read more
So far, we have discussed the architecture of Agent systems, how to organize the system into sub-Agents, and how to build a unified mechanism to standardize communication. Today, we will turn our attention to the tool layer and one of the most important aspects you need to consider in Agent system design: data retrieval. Data … Read more