LlamaIndex is a simple and flexible data framework that connects custom data sources with large language models. LlamaIndex provides comprehensive support for RAG. Advanced RAG (Retrieval-Augmented Generation) techniques can be modeled using a composable hierarchical abstraction. The retrieved text can be linked to the following elements: Retriever Text Pipeline Query Engine The retrieval of RAG … Read more
The RAG (Retrieval-Augmented Generation) model, commonly referred to as the RAG system, is widely used in large model applications. The principle of the model is quite simple: it retrieves information from a dataset based on user needs and then uses a large model for reasoning and generation. The advantage of RAG lies in its ability … 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
Today is January 22, 2025, Wednesday, Beijing, sunny. Today is the Little New Year in the North, and the Spring Festival is approaching. This article continues to explore the progress of GraphRAG, specifically looking at the RAG scheme that directly uses KG, which is KG-RAG, provided that the KG is established. Some ideas are worth … 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. 4. Overview of Existing RAG Frameworks Agent-Based RAG A new agent-based Retrieval-Augmented Generation (RAG) framework adopts a … 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
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