LlamaIndex
A linear, one-concept-per-lesson path through LlamaIndex, from Document/Node/Index fundamentals to a multi-document agentic RAG capstone with structured, validated output. 24 lessons · 3 tiers.
Prerequisites: Completion of LangChain & Agent Building through its RAG lessons (27-29), comfortable with embeddings, chunking, and similarity search. This course moves faster through those ideas and spends its explanations on what's actually new: LlamaIndex's own Document/Node/Index vocabulary.
Lessons use Google's Gemini free tier, the same as every other course in the repo: no new API key or service is required. LlamaIndex reuses the same GOOGLE_API_KEY from .env and the same uv sync-installed .venv, llama-index, llama-index-llms-google-genai, and llama-index-embeddings-google-genai are already in pyproject.toml.
Built on LlamaIndex, the open-source framework this course teaches.
Beginner
The core loop: Document to Node to Index to QueryEngine.
Intermediate
Structure, tools, and agents on top of the indexing core.
Advanced
Evaluation, workflows, and production concerns.