LlamaIndex in Python: A RAG Guide With Examples
Discover how to use LlamaIndex with practical examples. This framework helps you build retrieval-augmented generation (RAG) apps using Python. LlamaIndex lets you load your data and documents, create and persist searchable indexes, and query an LLM using your data as context.
In this tutorial, you’ll learn the basics of installing the package, setting AI providers, spinning up a query engine, and running synchronous or asynchronous queries against remote or local models.
By the end of this tutorial, you’ll understand that:
- You use LlamaIndex to connect your data to LLMs, allowing you to build AI agents, workflows, query engines, and chat engines.
- You can perform RAG with LlamaIndex to retrieve relevant context at query time, helping the LLM generate grounded