Introduction This sharing focuses on the implementation path and development prospects of multimodal RAG. The core topics cover five aspects: 1. Multimodal RAG based on semantic extraction 2. Multimodal RAG based on VLM 3. How to scale multimodal RAG based on VLM 4. Choice of technical routes 5. Q&A session Speaker｜Jin Hai Infiniflow Co-founder Editor｜Wang … Read more
In recent years, artificial intelligence has made significant leaps, primarily due to large language models (LLMs). LLMs are very good at understanding and generating human-like text, leading to the creation of various new tools, such as advanced chatbots and AI writers. Although LLMs excel at generating fluent, human-like text, they sometimes struggle with factual accuracy. … Read more
In today’s era of booming artificial intelligence, various models and applications emerge like mushrooms after rain. However, many common models and applications expose numerous flaws in practical use. Models such as o1, 4o, Claude, and applications like Sider mainly exist in the form of encapsulated interfaces, performing poorly in terms of timely knowledge updates, accurately … 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 enterprise 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. Reposted … Read more
Are you still struggling with RAG application development? Don’t worry, today I recommend five completely open-source and free RAG frameworks that cover various scenarios such as automatic optimization, multimodal processing, local deployment, and production environment support, helping you easily tackle RAG development! 👇 1. AutoRAG: Automatic Optimization, Worry-Free 🔑 Core Advantages: Automatically find the optimal … Read more
Abstract With the growing popularity of Retrieval-Augmented Generation (RAG) systems in various real-world services, concerns about their security are increasing. RAG systems enhance the generative capabilities of Large Language Models (LLMs) through retrieval mechanisms operating on private knowledge bases. However, unintended exposure of this mechanism can lead to severe consequences, including the leakage of private … Read more
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
Introduction Many product managers and engineers familiar with RAG often complain, “It only takes a week to produce a demo with RAG, but it takes at least six months to reach a production-level standard!” This is a realistic issue for the current industrial implementation of RAG. The RAG framework is very simple and understandable, and … 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