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
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
This article explains the relevant concepts of RAG, combined with code examples based on the “Building a Personal Knowledge Base with ERNIE SDK + LangChain”. Concept In 2020, the Facebook AI Research (FAIR) team published a paper titled “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”. This paper first introduced the concept of RAG, which is currently … Read more
With the continuous development of data intelligence technology, content generation technology represented by AIGC driven by large language models (LLM) has become an indispensable part of enterprises’ data intelligence capabilities. However, traditional content generation technologies face issues such as untimely information updates, lack of vertical domain knowledge, and model hallucinations. The Retrieval-Augmented Generation (RAG) technology … Read more
Originally from Akash Mathur’s Blog Abstract: In the field of information retrieval, Retrieval-Augmented Generation (RAG) models signify a paradigm shift, empowering large language models (LLMs) to generate responses that are rich in context and accurate. However, unlocking the full potential of RAG often transcends the limitations of its default query-retrieve-generate framework. This article delves into … Read more
GLM-PC is a general-purpose Agent technology product launched by Zhipu AI, based on a visual multimodal model that can simulate human operations on a computer. Below is an introduction and application scenarios for GLM-PC: Introduction GLM-PC can simulate basic operations of humans on computers, exploring the technology of general-purpose Agents based on visual multimodal models. … Read more
In the world of artificial intelligence and machine learning, intelligent query systems have become an indispensable part. Whether in search engines, recommendation systems, or customer service chatbots, we need a system that can intelligently understand and process user queries. LlamaIndex (formerly known as GPT Index) is a powerful Python library specifically designed to help developers … Read more
Are you still troubled by the uneven quality and poor performance of AI in China? Then let’s take a look at Developer Cat AI (3in1). This is an integrated AI assistant that combines GPT-4, Claude3, and Gemini into one. It covers all models of the three AI tools. Including GPT-4o and Gemini flash Now you … Read more
This is the sixth article in the large model programming series, and also my notes from the free course on some cloud large model engineer ACA certification[1]. This course is really good, highly recommended! 👍🏻 If you’re interested in the course, please click the link at the bottom to view the original article. Here are … Read more
In today’s rapidly advancing field of artificial intelligence, Retrieval-Augmented Generation (RAG) technology has become a hot topic for research and application due to its unique advantages. RAG technology combines the powerful generation capabilities of Large Language Models (LLMs) with efficient information retrieval systems, providing users with a new interactive experience. However, as the technology is … Read more