This article is about 11,000 words long and is recommended to be read in 6 minutes. This article introduces large models + RAG. 1 Introduction Large Language Models (LLMs) have limitations when handling domain-specific or highly specialized queries, such as generating inaccurate information or “hallucinations.” A promising approach to mitigate these limitations is Retrieval-Augmented Generation … Read more
Explore the diverse use cases of RAG across various fields, from enhancing customer support to analyzing financial markets. Retrieval-Augmented Generation (RAG) is a game-changing technology that combines artificial intelligence with information retrieval and language generation capabilities, enabling AI systems to provide users with accurate, data-driven responses. This approach is particularly effective in customer support, healthcare, … Read more
Click the “Blue WeChat Name” below the title to quickly follow In the era of large models, many new terms have emerged, and RAG is one of them. This article from the tech community, “Understanding RAG (Retrieval Augmented Generation) in One Article,” explains what RAG is, its functions, and the associated challenges. Related historical articles … Read more
A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future DirectionsThis article delves into the development of Retrieval-Augmented Generation (RAG), from basic concepts to the latest technologies. RAG effectively enhances output accuracy by combining retrieval and generation models, overcoming the limitations of LLMs. The study details the architecture of RAG, demonstrating how retrieval … Read more
In 2023, AI large models have sparked an unprecedented wave of technology, injecting new momentum into many industries. However, the AI large models currently on the market are mainly basic models, which, although possessing general knowledge capabilities, lack professional understanding of the legal industry. Many law firms and lawyers feel both eager and anxious, wanting … Read more
Key Points CAG (Cache-Augmented Generation) is a new paradigm that preloads all necessary information into the model’s context window, avoiding the real-time information retrieval delays and error risks associated with traditional RAG (Retrieval Augmented Generation). CAG uses a KV caching mechanism to preload the entire knowledge base as key-value pairs, enhancing response speed, accuracy, and … Read more
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