Learn LlamaIndex
Master the leading data framework for LLM applications. Learn document loaders, index types, query engines, RAG pipelines, and agents — connect your data to large language models.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is LlamaIndex? Data framework concepts, the ingestion-indexing-querying pipeline, and ecosystem overview.
2. Installation & Setup
Install LlamaIndex, configure LLM providers, load your first document, and run a basic query.
3. Indexing
Document loaders, node parsers, VectorStoreIndex, SummaryIndex, KnowledgeGraphIndex, and custom indexes.
4. Querying
Query engines, response synthesis, chat engines, streaming, and structured output from queries.
5. RAG Pipeline
Build production RAG with advanced retrieval, re-ranking, hybrid search, and evaluation strategies.
6. Agents
LlamaIndex agents, tool abstractions, ReAct agent, function calling, and multi-step reasoning.
7. Best Practices
Evaluation, cost management, chunking strategies, production deployment, and common pitfalls.
What You'll Learn
By the end of this course, you'll be able to:
Index Any Data
Load documents from PDFs, web pages, databases, and APIs. Parse, chunk, and index them for LLM consumption.
Query Intelligently
Build query engines that retrieve relevant context and synthesize accurate, well-sourced answers.
Build RAG Pipelines
Create production-grade RAG systems with advanced retrieval, re-ranking, and evaluation.
Create Agents
Build LlamaIndex agents that use tools, query multiple data sources, and reason step-by-step.