Building and deploying multi-agent RAG systems in 2024 with LlamaIndex

This is an introduction to LlamaIndex and in particular its agentic capabilities, covering:
What is LlamaIndex, including:
Frameworks in Python and TypeScript
LlamaParse service for parsing complex documents for LLMs
LlamaCloud service for end-to-end enterprise RAG
LlamaHub for free downloads of data connectors, LLM adapters, embedding models, vector stores, agent tools and more
Why you should use it, including:
Build faster
Eliminate boilerplate
Avoid early pitfalls
Get into production
Deliver business value
What can it do, including code examples of how to build:
Retrieval augmented generation (RAG)
World class parsing
Agents and multi-agent systems