Large Language Models often require access to specific, external information to provide relevant and accurate responses, especially when dealing with private datasets or knowledge beyond their initial training cutoff. Standard methods of feeding data directly into prompts can be inefficient or constrained by context window limitations. This chapter introduces LlamaIndex, a Python library specifically designed to manage the connection between LLMs and your external data sources.

You will learn the basic operations involved in using LlamaIndex:

• Ingesting data from various formats, such as documents and web pages.
• Structuring this data into optimized indexes for efficient retrieval.
• Querying these indexes to find relevant information for your LLM applications.

We will cover the fundamental concepts of LlamaIndex, including its core components like Nodes and Indexes, and practice loading, indexing, and retrieving information from your own data.