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