Wang Haofen, Tongji University, “Hundred Talents Program”, Distinguished Researcher, PhD Supervisor Personal Introduction: Wang Haofen, distinguished researcher and PhD supervisor in the “Hundred Talents Program” at Tongji University. He has served as CTO in frontline artificial intelligence companies for a long time. He is one of the initiators of OpenKG, the world’s largest Chinese open … Read more
This article primarily explains the paper “RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation”[1]. Currently, there are some relevant introductions available online, but most only discuss the ideas and mainly rely on GPT translations, which can be quite awkward and do not provide a detailed understanding of all principles. Therefore, a detailed description … Read more
Recently, while writing articles, I wanted to fill in some gaps left by last year’s RAG (Retrieval-Augmented Generation) and hope to share some tips to help everyone with RAG. As the old saying goes: Building a prototype of a large model is easy, but turning it into a product that can actually be put into … Read more
Original: https://zhuanlan.zhihu.com/p/682253496 Organizer: Qingke AI 1. Background Introduction The RAG (Retrieval Augmented Generation) method refers to a combination of retrieval-based models and generative models to improve the quality and relevance of generated text. This method was proposed by Meta in the 2020 paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”[1], allowing language models (LM) to acquire … Read more
This article explains the relevant concepts of RAG, combined with code examples based on the “Building a Personal Knowledge Base with ERNIE SDK + LangChain”. Concept In 2020, the Facebook AI Research (FAIR) team published a paper titled “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”. This paper first introduced the concept of RAG, which is currently … Read more
Introduction Hello everyone, I am Liu Cong from NLP. As the context length supported by large models continues to increase, a debate has emerged online (many groups are discussing this topic, so I would like to share my thoughts) regarding RAG and Long-Context, which is really unnecessary… The main point is that the two are … Read more
Click the blue text above to follow us 1. Introduction In the wave of artificial intelligence, Retrieval-Augmented Generation (RAG) technology has become a hot topic in research and application due to its unique advantages. RAG technology combines the powerful generative capabilities of large language models (LLMs) with efficient information retrieval systems, providing users with a … Read more
1. RAG (Retrieval Augmented Generation) RAG technology is a method that combines retrieval and generation. It typically relies on two core components: a large language model (such as GPT-3) and a retrieval system (such as a vector database). RAG first uses the retrieval system to extract relevant information from a vast amount of data, then … Read more
In a previous article titled RAG Mastery Manual: Is RAG Sounding the Death Knell? Does Long Context in Large Models Mean Vector Retrieval is No Longer Important, we introduced the indispensability of RAG in solving the hallucination problem of large models, and reviewed how to enhance the practical effects of RAG using vector databases. Today, … Read more
1. Introduction Since leaving the last company with Yuanwai, we started our own company focusing on the development of RAG large model AI product applications. During this period, which included a Spring Festival, the total time was about three months. We worked day and night, and as of the end of March, the product has … Read more