Agentic RAG

RAG-Check: A Novel AI Framework for Multimodal Retrieval-Augmented Generation

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RAG-Check: A Novel AI Framework for Multimodal Retrieval-Augmented Generation

Large Language Models (LLMs) have made significant progress in the field of generative artificial intelligence, but they face the “hallucination” problem, which is the tendency to generate inaccurate or irrelevant information. This issue is particularly severe in high-risk applications such as medical assessments and insurance claims processing. To address this challenge, researchers from the University … Read more

Introduction to RAG Technology: A New Journey in Large Model Application Development

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Introduction to RAG Technology: A New Journey in Large Model Application Development

What is RAG Technology RAG, or Retrieval-Augmented Generation technology, is an AI architecture that combines retrieval and generation. It enhances the output quality of language models by retrieving external knowledge. This is akin to equipping large language models with an intelligent knowledge base, enabling them to retrieve and reference relevant information in real-time while generating … Read more

Roaming RAG Technology: Features and Advantages

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Roaming RAG Technology: Features and Advantages

Roaming RAG, as an innovative RAG technology, can be elaborated on in detail regarding its characteristics and advantages from the following aspects: Working Principle and Process The core of Roaming RAG lies in utilizing the hierarchical structure of documents to enhance the information retrieval capabilities of large language models (LLMs). The specific process includes: Document … Read more

RAG Architecture Explained: 7 Patterns from Basic to Advanced

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RAG Architecture Explained: 7 Patterns from Basic to Advanced

The RAG technology introduces external knowledge retrieval in the AI generation process, evolving from basic document queries to intelligent architectures with multi-modal and Multi-Agent collaborations, allowing AI to provide more accurate and comprehensive answers. Core Components: Embedding Model: Converts text into vector representations. Generation Model: Responsible for the final content generation. Re-ranking Model: Optimizes the … Read more

Unlocking Efficient Data Retrieval with Query Construction Techniques in RAG Systems

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Unlocking Efficient Data Retrieval with Query Construction Techniques in RAG Systems

Click 👇🏻 to follow, article from “ With the expanding application of large language models (LLMs), Retrieval-Augmented Generation (RAG) has become a mature technology. The popularity of products like txt2sql and ChatBI highlights the increasing importance of query construction techniques. This article analyzes the process of query construction and illustrates, through examples, how to transform … Read more

Mastering RAG Series 2: Query Translation Techniques

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Mastering RAG Series 2: Query Translation Techniques

LLM (Large Language Model) is a powerful new platform, but they are not always trained on data that is relevant to our tasks or the most recent data. RAG (Retrieval Augmented Generation) is a general method that connects LLMs with external data sources (such as private data or the latest data). It allows LLMs to … Read more

Overview of 15 Classic RAG Frameworks (Part 2)

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Overview of 15 Classic RAG Frameworks (Part 2)

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. 4. Overview of Existing RAG Frameworks Agent-Based RAG A new agent-based Retrieval-Augmented Generation (RAG) framework adopts a … Read more

Mastering RAG: The Basics of Retrieval-Augmented Generation

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Mastering RAG: The Basics of Retrieval-Augmented Generation

LLM (Large Language Model) is a powerful new platform, but they are not always trained on data relevant to our tasks or the latest data. RAG (Retrieval Augmented Generation) is a general method that connects LLMs with external data sources (such as private or up-to-date data). It allows LLMs to use external data to generate … Read more

RAG vs Fine-Tuning: A Guide for Domain-Specific AI Models

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RAG vs Fine-Tuning: A Guide for Domain-Specific AI Models

Machine Heart Report Editor: Rome Retrieval-Augmented Generation (RAG) and Fine-tuning are two common methods to enhance the performance of large language models. So, which method is better? Which is more efficient when building applications in specific domains? This paper from Microsoft serves as a reference for your choice. When constructing large language model applications, there … Read more