RAG in 2024: Advancing to Agents

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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

LlamaIndex

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Loved this video, exactly what I’m looking for in how to improve my RAG pipeline

statstutorials

Extremely well put and explained. Thank you

mili

Great work! Do you have any content like this with GraphRAG?

bongimusprime

You sir, are a gentleman and a scholar

shacafa

I'm curious about the speed of reasoning for Language Agent Tree Search reasoning. Could you provide some rough numbers in terms of what we can expect? Maybe with Open AI or Groq?

cagdasucar

Can it work just like Google NotebookLM or it's different?

fslurrehman

A "putting it all together" example on github would be much appreciated.

feralmachine

Loved the video, but the link to the resources is not working properly :(

juliocesar

Great presentation!
However, it would be so much more practical to see things in practical code, for example a multi-agent project.

hoangng

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