LlamaIndex
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LlamaIndex

Agents that reason over your documents. Strong on retrieval and indexing.

30 lessons3h 27m reading6 sections
01Retrieval from first principles0/5 · 26m
LlamaIndex overview4m
Installation and setup5m
Why retrieval: a model does not know your documents5m
Embeddings: text as numbers that carry meaning6m
VectorStoreIndex: searching documents by meaning6m
Section quiz · 5 questionsNot taken
02Loading and chunking0/4 · 25m
SimpleDirectoryReader: loading a folder of documents6m
Chunking documents with SentenceSplitter7m
Metadata: what gets embedded and what the model sees6m
IngestionPipeline: load, split and embed once6m
Section quiz · 5 questionsNot taken
03Querying0/7 · 46m
Retrievers: the search step on its own6m
Query engines: the prompt the model receives6m
Answers with sources: a stand-in model and citations7m
Refusing when nothing fits: similarity cutoffs6m
Metadata filters: who may see which documents7m
Structured output: a typed answer with Pydantic7m
Chat engines: remembering the conversation7m
Section quiz · 5 questionsNot taken
04Better retrieval0/4 · 26m
BM25: when exact words matter6m
Hybrid search: keywords and meaning together6m
Reranking: a second, closer look at the top results7m
Measuring retrieval: hit rate on labelled questions7m
Section quiz · 5 questionsNot taken
05Agents and workflows0/6 · 55m
FunctionAgent: an agent that queries your index9m
Agent tools: giving an agent more than search9m
Long-term memory: recalling past sessions9m
Workflows: a custom RAG pipeline with @step10m
Multi-agent: routing between two agents10m
Observability: tracing what a run did8m
Section quiz · 3 questionsNot taken
06Shipping a RAG app0/4 · 29m
Persisting an index: not embedding twice5m
Updating documents: refresh and delete6m
Integrations: swapping in real backends8m
LlamaIndex project: a help-centre assistant10m
Section quiz · 5 questionsNot taken