ff ↑ Subscribe to us, get a wealth of free tutorial resources 1. What is RAG? – A Super Assistant That Can Retrieve and Generate Have you ever encountered this problem: when asking a large model, it can answer many questions, but sometimes it also “makes things up” or only provides information based on its … 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
In the field of artificial intelligence, large language models (LLMs) have achieved significant accomplishments. However, due to their reliance on static training data, they often struggle to respond effectively to dynamic real-time queries. Retrieval-Augmented Generation (RAG) technology has emerged, bringing new hope to address this issue. Agentic RAG further breaks through the limitations of traditional … Read more
Reply ‘data’ to receive a collection of algorithm interview questions (large models, deep learning, machine learning). 1. What is Retrieval-Augmented Generation (RAG)? RAG is a hybrid approach that combines retrieval systems and generative language models. It consists of two steps: Retrieval Component: Searches for relevant information in large external corpora or datasets based on the … Read more
LLM (Large Language Model, a powerful new platform) is a powerful new platform, but they are not always trained on data relevant to our tasks or the latest data. RAG (Retrieval Augmented Generation, a method that connects LLMs with external data sources (such as private data or the latest data). It allows LLMs to use … Read more
Click the “blue text” to follow us In today’s rapidly changing artificial intelligence (AI) technology landscape, major tech companies are launching innovative products aimed at providing smarter and more efficient solutions for enterprises and individual developers. Recently, Google Cloud announced the full launch of its Vertex AI RAG Engine (Retrieval-Augmented Generation Engine), which has garnered … Read more
Hello, I am the Fisherman. Today, I am sharing a 35-page overview of the latest Agentic RAG. The core problem this paper aims to address is the outdated, inaccurate outputs, and hallucinations that arise when today’s large language models (LLMs) rely on static training data to handle dynamic, real-time queries. It starts from the fundamental … Read more
How to manage personal knowledge in the AI era? Quivr gives us a glimpse. Today, we introduce an open-source software for personal knowledge management using AI: Quivr. GitHub link: https://github.com/QuivrHQ/quivr Its cloud-deployed product: https://quivr.app/ Product Slogan 🚀 The official slogan of Quivr is “Your GenAI Second Brain,” emphasizing that this project provides users with a … Read more
Previously, I have written many articles introducing RAG implemented based on Azure OpenAI. This article introduces the implementation of RAG through Phi-2 and LlamaIndex. LlamaIndex is an open-source framework that effectively builds LLM applications when used in conjunction with Hugging Face Transformers, providing convenient methods for setting up databases and retrievers. The community activity of … Read more
Phidata is a framework designed for building AI agents with memory, knowledge, and tools. https://www.phidata.com/ https://github.com/phidatahq/phidata https://docs.phidata.com/introduction Three Aspects of Phidata Enhancing LLM Functionality: Memory: Phidata stores chat history in a database, allowing large language models to support longer conversations, thereby better understanding and tracking the context of dialogues. Knowledge: By storing business-relevant information in … Read more