When we talk about the complex and fascinating modern technological concept of knowledge graphs, we can cleverly compare it to the family tree of a large family in our daily lives. 1. Family Tree Imagine you have opened an ancient book filled with history and stories; this is a family tree that records the lineage … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic community, industry, and enthusiasts in machine learning and natural language processing, especially for beginners. … Read more
Source: DeepHub IMBA This article is about 1600 words long and is recommended to be read in 5 minutes. Retrieval-Augmented Generation is a technical approach aimed at enhancing the performance of large language models. Overview of Retrieval-Augmented Generation (RAG) Technology Retrieval-Augmented Generation (RAG) is a technical method aimed at enhancing the performance of Large Language … 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
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