Retrieval-Augmented Generation (RAG) is an emerging AI technology stack that enhances the capabilities of large language models (LLMs) by providing additional “up-to-date knowledge”. The basic RAG application includes four key technical components: Embedding Model: Used to convert external documents and user queries into embedding vectors Vector Database: Used to store embedding vectors and perform vector … Read more
Click on “Deephub Imba“, follow the public account, and don’t miss out on great articles! There have been many articles about Retrieval-Augmented Generation (RAG). If we could create a trainable retriever, or if the entire RAG could be customized like fine-tuning a large language model (LLM), we would definitely achieve better results. However, the current … Read more
Abstract: The transition from traditional RAG to Graph RAG enhances large language models by integrating knowledge graphs, enabling them to provide more detailed and accurate responses to complex queries. The effectiveness of Graph RAG also depends on the quality and breadth of the underlying knowledge graph and the engineering aspects of RAG. Main Points: – … Read more
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
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
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
With the continuous development of data intelligence technology, content generation technology represented by AIGC driven by large language models (LLM) has become an indispensable part of enterprises’ data intelligence capabilities. However, traditional content generation technologies face issues such as untimely information updates, lack of vertical domain knowledge, and model hallucinations. The Retrieval-Augmented Generation (RAG) technology … Read more