Source: DeepHub IMBA This article is about 2500 words long and suggests a reading time of 9 minutes. This paper proposes a new scheme that utilizes the multi-head attention layer of the decoder model instead of the traditional feed-forward layer activation. The existing RAG solutions may suffer because the embeddings of the most relevant documents … Read more
Source: DeepHub IMBA This article is about 3000 words long and is recommended to be read in 5 minutes. This article will explore three effective techniques to enhance document retrieval in applications based on <strong>RAG</strong>. By combining these techniques, it is possible to retrieve documents that closely match user queries, thus generating better answers. The … Read more
Book Giveaway at the End Since RAG was introduced by Facebook AI Research in 2020, it has rapidly gained popularity. After all, it has truly been a great help, playing a key role in solving the “hallucination” problem of large language models. Today, tech giants like Google, AWS, IBM, Microsoft, and NVIDIA are all supporting … 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
This article introduces a practical project that uses LangGraph to develop a RAG research multi-agent tool. This tool is designed to solve complex problems that require multiple sources and iterative steps to arrive at a final answer. It employs hybrid search and Cohere reordering steps to retrieve documents, and also includes a self-correction mechanism, including … Read more
In this tutorial, we will learn how to build an intelligent document retrieval system using langgraph. This system can extract information from web pages, perform intelligent segmentation, and achieve precise Q&A functionality through query analysis and vector retrieval. 1. Install Dependencies <span>pip install beautifulsoup4</span> 2. Import Necessary Libraries import bs4 from typing import Literal from … Read more