2024年3月12日 · The document describes the RAG (Retrieval-Augmented Generation) model for knowledge-intensive NLP tasks. RAG combines a pre-trained language generator (BART) with a dense passage retriever (DPR) to retrieve and incorporate relevant knowledge from Wikipedia.

本文从RAG架构入手，详细介绍相关技术细节，并附上一份实践案例。 什么是RAG? 检索增强生成（Retrieval Augmented Generation），简称 RAG，已经成为当前最火热的LLM应用方案。 经历今年年初那一波大模型潮，想必大家对大模型的能力有了一定的了解，但是当我们将大模型应用于实际业务场景时会发现，通用的基础大模型基本无法满足我们的实际业务需求，主要有以下几方面原因： 知识的局限性：模型自身的知识完全源于它的训练数据，而现有的主流大模 …

One way of improving the LLM results is called “retrieval-augmented generation” or RAG. In this video, IBM Senior Research Scientist Marina Danilevsky explains the LLM/RAG framework and how this combination delivers two big advantages, namely: the model gets the most up-to-date and trustworthy facts, and you can see where the model got its ...

2023年12月6日 · With the advent of multi-modal LLMs like GPT-4V, it is now becoming possible to unlock RAG on the visual content often captured in slide decks. Below, we show two different ways to tackle this problem. We share a public benchmark for evaluating RAG on slide decks and use it highlight the trade-offs between these approaches.

腾讯云向量数据库《 RAG 七天入门训练营》学习笔记. Contribute to jiewong42/Notes-on-Tencent-Cloud-Vector-Database-RAG-Course development by creating an account on GitHub. ... Notes-on-Tencent-Cloud-Vector-Database-RAG-Course / ppt / 第一节-“消灭”LLM幻觉的利器 - RAG介 …

RAG, short for Retrieval Augmentation Generation, is a powerful model that combines the capabilities of retrieval, augmentation, and generation in natural language processing tasks. It is designed to enhance the performance of question-answering systems and text generation models by integrating these three key components.

What is LLM (Large Language Model) is a type of artificial intelligence (AI) program that can recognize and generate text/image/video. LLMs are trained on huge sets of data —hence the name "large." LLMs are built on machine learning: specifically, a type of neural network called a transformer model. Example: GPT4, Google Gemini, Llama3

2024年6月1日 · It highlights the limitations of traditional RAG approaches, introduces the benefits of graph-based RAG (GraphRAG), and details its architecture, use cases, and tools. Key applications include chatbots for finance, healthcare, and customer support, leveraging graph databases like FalkorDB for low-latency and dynamic knowledge retrieval.

Use LLM (or other models) to extract key entities from the question. Retrieve subgraphs based on entities, delving to a certain depth, such as 2 hops or even more. Utilize the obtained context to generate answers through LLM.

2024年9月25日 · What is the role of a rag agent LLM in RAG? A rag agent LLM (Large Language Model) plays a crucial role in RAG by enhancing the generative capabilities through advanced language understanding and generation, making the retrieval process more efficient and accurate.