Overview As most LLMs are only trained periodically on a large amount of public data, they cannot access the latest information and/or private data. Retrieval-Augmented Generation (RAG) is a core paradigm for developing applications with LLMs, addressing this issue by connecting to external data sources. A basic RAG pipeline includes embedding user queries, retrieving relevant … Read more
▼Recently, there have been a lot of live broadcasts,make an appointment to ensure you gain something. The RAG (Retrieval-Augmented Generation) technology is detailed in the article “Understanding RAG: A Comprehensive Guide to Retrieval-Augmented Generation,” with a typical RAG case shown in the image below, which includes three steps: Indexing: Split the document library into shorter … Read more
This project is the open knowledge base of the OpenRAG subproject under OpenKG, which focuses on RAG research and is committed to promoting the development of the RAG field. You can access it through the link to enter the OpenRAG Base homepage. Origin Retrieval-Augmented Generation (RAG), as one of the most concerned practical technologies for … Read more
Effectively parsing and understanding tables in unstructured documents remains a significant challenge when designing RAG solutions. This is especially difficult in cases where tables exist in image formats, such as scanned documents. These challenges include several aspects: The complexity of scanned or image documents, such as diverse structures, the presence of non-text elements, and the … Read more
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