If you are building a retrieval-augmented generation (RAG) application, you know how powerful they can be when they work well. But semantic embedding models are not magic. Most RAG implementations rely solely on semantic similarity as the unique retrieval mechanism, throwing every document into a vector database and applying the same retrieval logic to each … Read more
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 While LLMs excel in natural language processing, their reliance on static pre-trained data often results in outdated or incomplete responses.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
In today’s rapidly advancing field of artificial intelligence, Retrieval-Augmented Generation (RAG) technology has become a hot topic for research and application due to its unique advantages. RAG technology combines the powerful generation capabilities of Large Language Models (LLMs) with efficient information retrieval systems, providing users with a new interactive experience. However, as the technology is … Read more
LLM (Large Language Model, a powerful new platform) 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, a method that connects LLMs with external data sources (such as private data or the latest data). It allows LLMs to use … Read more
Hello, I am the Fisherman. Today, I am sharing a 35-page overview of the latest Agentic RAG. The core problem this paper aims to address is the outdated, inaccurate outputs, and hallucinations that arise when today’s large language models (LLMs) rely on static training data to handle dynamic, real-time queries. It starts from the fundamental … 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
Click Follow us for updates in blue above Cover image: A recent cognitive class on intelligence by the author, demystifying large language models from a comparative perspective “ 𝕀²·ℙarad𝕚g𝕞 Intelligent Square Paradigm Research: Writing to Deconstruct Intelligence。 After all, deep learning LLMs are not the entirety of AI, and the path to AGI is not … Read more
Selected from interconnects Author: Nathan Lambert Translated by Machine Heart Machine Heart Editorial Team State-space models are on the rise; has attention reached its end? In recent weeks, there has been a hot topic in the AI community: implementing language modeling with attention-free architectures. In short, this refers to a long-standing research direction in the … Read more
Big Data Digest authorized reprint from Xi Xiaoyao Technology Author | Xie Nian Nian In interpersonal communication, especially when using a language as profound as Chinese, people often do not answer questions directly but instead adopt implicit, obscure, or indirect expressions. Humans can make accurate judgments about some implied meanings based on past experiences or … Read more
In the previous article, I introduced how to deploy large models locally on Mac computers. By customizing prompts, various needs such as domain extraction can be achieved. However, in reality,<span><span> deploying large models locally</span></span> is not very friendly for individual developers. On one hand, it requires a significant investment to ensure that the hardware has … Read more