✨New article published👉 "Demystifying RAG-Empowered Chat Agents - Part 1" 🙋How Hypothetical Document Embeddings solve common alignment challenges in AI-powered chat agents? Read more:https://t.co/L56zLdxlIZ #AI #RAG #HyDE #AIAgents https://t.co/d54VZrYFNV
2023: RAG is all you need 2024: AI agents are all you need Now bringing you the fusion of AI agents and RAG pipelines. In our recent blog post @ecardenas300 and I lift the curtains of agentic RAG. We discuss: • What is agentic RAG • Architectures of agentic RAG • How to… https://t.co/1VA89kZ03G
What makes an agentic system different from vanilla RAG? It’s access to memory and external tools. The building blocks of an agentic RAG system are: • LLM (with a role and a task) • Memory (short-term and long-term) • Planning (e.g., reflection, self-critics, query routing,… https://t.co/vXUzTAZNAG
Yurts has introduced a high-performing AI system using their RAG (Retrieval-Augmented Generation) technology, which is being compared to a new algorithm from Anthropic. Co-founder Guruprasad Raghavan and senior AI researcher Kartik Gupta have detailed how the Yurts RAG system operates. Meanwhile, there is a growing interest in agentic RAG systems, which differ from standard RAG by incorporating tools, memory, and planning capabilities. These agentic systems allow for complete autonomy in reasoning and executing tasks, making them a significant advancement in AI technology. The concept of agentic RAG, its implementation, and its benefits and limitations have been highlighted in recent discussions and articles, including a deep dive at TechCrunch Disrupt 2024 by Madhukar Kumar. Additionally, Multi-Agent RAG systems and RAG-Empowered Chat Agents using Hypothetical Document Embeddings are also gaining attention in the AI community.