Agentic RAG

Overview of Agentic Retrieval-Augmented Generation

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Overview of Agentic Retrieval-Augmented Generation

Large language models (LLMs) have revolutionized the field of artificial intelligence (AI) by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their responsiveness to dynamic real-time queries, resulting in outdated or inaccurate outputs. Retrieval-Augmented Generation (RAG) serves as a solution by integrating real-time data retrieval to enhance … Read more

MedGPT: Demonstrating Excellent Medical Performance Based on RAG Evaluation Framework

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MedGPT: Demonstrating Excellent Medical Performance Based on RAG Evaluation Framework

The Retrieval-Augmented Generation (RAG) technology is revolutionizing the AI application field by integrating external knowledge bases with internal knowledge of LLM (Large Language Model), enhancing the accuracy and reliability of AI systems. The knowledge “recall ability” of the multimodal knowledge extractor directly determines whether the large model can obtain accurate professional knowledge when answering reasoning … Read more

Comprehensive Overview of Agentic RAG

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Comprehensive Overview of Agentic RAG

https://arxiv.org/pdf/2501.09136 Overview of Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) represents a significant advancement in the field of artificial intelligence by combining the generative capabilities of Large Language Models (LLMs) with real-time data retrieval. While LLMs excel in natural language processing, their reliance on static pre-trained data often results in outdated or incomplete responses. RAG achieves … Read more

How to Handle Table Data in RAG Knowledge Base Documents?

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How to Handle Table Data in RAG Knowledge Base Documents?

In developing the RAG system, the data formats in the knowledge base can be diverse, and most of them are unstructured data content. For example, PDF documents in the knowledge base are likely to contain table data, and our approach to handling this needs special attention to ensure that the table information can be correctly … Read more

Reject Module in Large Model RAG

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Reject Module in Large Model RAG

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

RAG 2.0 Performance Improvement: Strategies and Practices for Optimizing Indexing and Recall Mechanisms

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RAG 2.0 Performance Improvement: Strategies and Practices for Optimizing Indexing and Recall Mechanisms

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

Knowledge Notes on Large Models RAG & Agent

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Knowledge Notes on Large Models RAG & Agent

“ 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

Alibaba: AirRAG Enhances Complex QA Reasoning Capabilities

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Alibaba: AirRAG Enhances Complex QA Reasoning Capabilities

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

Introduction to RAG in Large Models

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Introduction to RAG in Large Models

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

Summary and Implementation Ideas of RAG Chunking Technology

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Summary and Implementation Ideas of RAG Chunking Technology

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