Recommendation This article introduces the book “Generative AI in Action“, published by O’Reilly in 2024, authored by Amit Bahree from Microsoft. The book mainly covers knowledge related to generative AI, including LLMs, prompt engineering, model fine-tuning, RAG, vector databases, etc. The author provides numerous code examples, guiding readers into the world of generative AI through … Read more
XRAG supports comprehensive RAG evaluation benchmarks and toolkits, covering over 50 testing metrics for thorough evaluation and optimization of failure points in RAG. It supports comparisons among four types of advanced RAG modules (query rewriting, advanced retrieval, question-answering models, post-processing) and integrates various specific implementations within the modules, supporting the OpenAI large model API. The … Read more
Datawhale Insights Author: Song Zhixue, Datawhale Member Hello everyone, I am not a fan of garlic and ginger. Next, I will guide you step by step to implement a simple RAG model, which is a simplified version of RAG, called Tiny-RAG. Tiny-RAG is a simplified version of RAG that only includes the core functions of … Read more
Introduction Hello everyone, I am Liu Cong from NLP. RAG (Retrieval-Augmented Generation) finds information fragments relevant to user questions through a retrieval system, utilizing large models to synthesize an answer. This greatly addresses issues such as hallucination and outdated information in large models, and has become an important means for the practical application of large … Read more
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