AI Agent 
Introduction This sharing is titled “RAG 2.0 Engine Design Challenges and Implementation”. Main content includes the following parts: 1. Pain points and solutions of RAG 1.0 2. How to effectively Chunking 3. How to accurately recall 4. Advanced RAG and preprocessing 5. How RAG will develop in the future 6. Q&A Guest Speaker｜Zhang Yingfeng Founder … Read more
To effectively implement <span>RAG</span>, there are indeed many aspects that need refinement, and today we will learn about the Reject Module. Official Explanation In the RAG (Retrieval-Augmented Generation) model, the Reject Module is an important component designed to enhance the robustness of the generation model when facing irrelevant queries or information. Plain Explanation A simple … Read more
What Cross-Modal Scenarios Does GraphRAG Support? No Small Talk, Straight to the Point GraphRAG (Graph-based Retrieval-Augmented Generation) is a framework that combines knowledge graphs and retrieval-augmented generation technology, effectively handling cross-modal scenarios and supporting various complex data types and application scenarios. Below, we will introduce the main cross-modal scenarios supported by GraphRAG. 1. Text-Image Question … Read more
“ Hello everyone, this is Goodnote. The knowledge notes on large models RAG & Agent have been updated. The total word count is over 50,000. Due to space limitations, this article will only provide a summary. For detailed notes, please enter our public account and reply with ‘RAG’ and ‘Agent’ to obtain them. RAG Notes … Read more
This is the sixth article in the large model programming series, and also my notes from the free course on some cloud large model engineer ACA certification[1]. This course is really good, highly recommended! 👍🏻 If you’re interested in the course, please click the link at the bottom to view the original article. Here are … Read more
Alibaba: AirRAG Enhances Complex QA Reasoning Capabilities! 🌟 Introduction 1️⃣ As the complexity of tasks increases, RAG faces new challenges, including the difficulty of retrieving sufficient knowledge in a single query and understanding the complex reasoning logic in questions. 2️⃣ This article proposes AirRAG, which activates intrinsic reasoning capabilities and expands the solution space by … Read more
ff ↑ Subscribe to us, get a wealth of free tutorial resources 1. What is RAG? – A Super Assistant That Can Retrieve and Generate Have you ever encountered this problem: when asking a large model, it can answer many questions, but sometimes it also “makes things up” or only provides information based on its … Read more
TrustRAG Project Address🌟: **https://github.com/gomate-community/TrustRAG** Configurable Modular RAG Framework Introduction to Chunking In the RAG (Retrieval-Augmented Generation) task, chunking is a critical step, especially when dealing with complex PDF documents.PDF documents may contain images, strange layouts, etc., which increases the difficulty of chunking. Impact of Chunk Granularity Sentence/Word Granularity: Focuses on local, key information queries but … Read more
In the field of artificial intelligence, large language models (LLMs) have achieved significant accomplishments. However, due to their reliance on static training data, they often struggle to respond effectively to dynamic real-time queries. Retrieval-Augmented Generation (RAG) technology has emerged, bringing new hope to address this issue. Agentic RAG further breaks through the limitations of traditional … Read more
Today, I will share a retrieval-augmented generation method designed for resource-constrained scenarios: MiniRAG. Paper link: https://arxiv.org/pdf/2501.06713 Code link: https://github.com/HKUDS/MiniRAG Introduction With the rapid development of retrieval-augmented generation (RAG) technology, the performance of language models in knowledge retrieval and generation tasks has significantly improved. However, existing methods heavily rely on large language models (LLMs), leading to … Read more