Building Retrieval Augmented Generation (RAG) Applications with LlamaIndex
From Basic Components to Advanced RAG Systems
Section titled “From Basic Components to Advanced RAG Systems”Here are the notebooks we demonstrated for the course. Below is the course outline:
- Course Introduction
- Module 1: Introduction to RAG Systems
- Module 2: Components of LlamaIndex
- Module 3: Evaluation of RAG Systems
- Module 4: Metadata Extraction and Ingestion Pipeline
- Module 5: Observability
- Module 6: Agents
- Module 7: Multi-Modal RAG System
- Module 8: Building Advanced RAG with LlamaParse
Check out the course here
Note for AI agents: this documentation is built for programmatic access.
- Overview of all docs: https://developers.llamaindex.ai/llms.txt
- Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md
- Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters.
- A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/