Recently, Retrieval-Augmented Generation (RAG) has garnered widespread attention in the AI field, becoming a focal point of discussion among many researchers and developers. As a technology that combines retrieval with generation, RAG demonstrates the potential to achieve outstanding results in various tasks such as question answering, dialogue generation, and text summarization. Its emergence provides a … Read more
As one of the most advanced technologies in artificial intelligence, Retrieval-Augmented Generation (RAG) technology can provide reliable and up-to-date external knowledge, offering great convenience for numerous tasks. Especially in the era of AI-Generated Content (AIGC), RAG’s powerful retrieval capabilities in providing additional knowledge enable it to assist existing generative AI in producing high-quality outputs. Recently, … Read more
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
Many practitioners have found that although RAG can quickly build a demo in a short time, it faces numerous challenges in actual production environments. This article analyzes the core issue of RAG’s industrial implementation from the perspective of entrepreneurs in the AI large model field—problem grading—and discusses the challenges and solutions of four types of … Read more
Happy New Year, everyone! Today, I will interpret Baichuan’s RAG approach. Baichuan Intelligent has a profound background in search; let’s see how they navigated the pitfalls of RAG! In general, Baichuan combines a long context model (192k) with search enhancement methods to address knowledge updates and reduce model hallucinations, achieving 95% accuracy on a dataset … Read more
Reprinted from WeChat Official Account | Blue’s Little Firefly In this article, I would like to accurately introduce the application areas of Knowledge Graphs (KG) in the RAG pipeline. We will explore the different types of questions that arise in the RAG pipeline and how to address these issues by applying knowledge graphs at various … Read more
In RAG, after reading the files, the main task is to split the data into smaller chunks and then embed these features to express their semantics. The location of this process in RAG is shown in the figure below. The most common chunking method is rule-based, using techniques such as fixed chunk sizes or overlapping … Read more
Original: https://zhuanlan.zhihu.com/p/682253496 Compiled by: Qingke AI Leave a message in the backend ‘ Exchange ‘, Join the NewBee discussion group 1. Background Introduction 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 … 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