Source: DeepHub IMBA This article is approximately 1800 words and is suggested to be read in 6 minutes. This article will introduce how to integrate and create a scalable and customizable agent RAG. Building large language model applications can be quite challenging, especially when we have to choose between different frameworks like LangChain and LlamaIndex. … Read more
Photographer: Product Manager Fried Crab Shell When it comes to RAG or Agent, many people immediately think of LangChain or LlamaIndex, as they seem to believe these two are standard tools for developing applications with large models. But for me, I particularly dislike these two. Because they are the typical representatives of over-encapsulation. Especially with … Read more
Lu Xingguo Ye Xiangjun (2)T−B− SCID 1. Recombinase-activating Genes1 and 2 Deficiency Recombinase-activating genes1 and 2 (recombinase-activating genes 1 and 2，RAG1/2) deficiency accounts for approximately6% of SCID patients. In about75% of patients with RAG1/2 deficiency, there are very low numbers of T, B lymphocytes, and NK cells. This type of SCID is caused by mutations … Read more
Author: Liu Ran Affiliation: China People’s Police University, Information Technology and Network Management Department Editorial Note In the wave of digital transformation, the information construction of universities is in full swing, with AI applications represented by large language models becoming an important topic for the construction of smart campuses. The One-Stop Service Platform, as an … Read more
Source: DeepHub IMBA This article is about 3200 words long, and it is recommended to read for 6 minutes. This article explores the new integration of LlamaIndex for fine-tuning OpenAI's GPT-3.5 Turbo. OpenAI announced on August 22, 2023, that fine-tuning of GPT-3.5 Turbo is now possible. This means we can customize our own models. Subsequently, … Read more
This is the 9th article by Teacher Wang Jue introducing AIGC. Please refer to the previous articles: #ai Teacher Wang Jue’s AIGC Educational Application Collection —————————————— Although large models are amazing, they can help us with general document work But Teacher Wang Jue has always believed: For professional research and education, large models are not … Read more
Source: DeepHub IMBA This article is approximately 3300 words long and is recommended for a 5-minute read. In this article, I will use two frameworks to complete some basic tasks in parallel. When using large models locally, especially when building RAG applications, there are generally two mature frameworks available: LangChain: A general framework for developing … Read more
BioRAG: A RAG-LLM Framework for Biological Question Reasoning The question-answering systems in the life sciences face challenges such as rapid discovery, evolving insights, and complex interactions of knowledge entities, necessitating a comprehensive knowledge base and precise retrieval. To address this, we introduce BioRAG, a retrieval-augmented generation framework that combines large language models. First, we parse, … Read more
Follow our public account to discover the beauty of CV technology This article shares the ACL 2024 paper PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers, open-sourced by the Cambridge University team, empowering multi-modal large model RAG applications, and is the first pre-trained general multi-modal late-interaction knowledge retriever. Paper link: https://arxiv.org/abs/2402.08327 Project homepage: https://preflmr.github.io/ Introduction The … Read more
Source: Biological Large Models This article is approximately 3000 words long and is suggested to be read in 5 minutes. This article introduces an innovative biological question reasoning system that combines Retrieval-Augmented Generation (RAG) and Large Language Models (LLM). In today’s rapidly advancing life sciences field, efficiently processing and answering complex biological questions has always … Read more