RAG in 2024: Advancing to Agents скачать в хорошем качестве

RAG in 2024: Advancing to Agents

I'm Laurie, VP of Developer Relations at Llama Index. If you've spent time with LlamaIndex, you already know about the importance of retrieval-augmented generation or RAG. In this video, I make the case that while RAG is necessary, it's not enough for sophisticated knowledge retrieval. You need to build an agent. In this video we cover: Basic RAG Agentic components, including Routing Memory Planning Tool use Agentic reasoning, including Sequential (like Chain of Thought) DAG-based Tree-based (like Tree of thought) And we briefly cover further extensions including Observability Controllability Customizability You can find links to all the resources covered in this video at https://bit.ly/li-agent-resources