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Tutorials

Worked examples of building with LanceDB, grouped by what they build.

Every tutorial is one page in the sidebar, grouped by what it builds. The documentation explains how a feature works; these show it doing something.

Examples marked as notebooks live in the vectordb-recipes repository and are runnable in Colab; the rest are written up here.

Where to start

SearchVector, multi-vector, hybrid and full-text retrieval.RAGRetrieval-augmented generation and agent applications.Feature engineeringDeriving columns and materialized views.TrainingCurating data and training models directly on a table.

The features these use

Feature	What it covers
Vector search	Similarity search, distance metrics, and tuning it.
Full-text search	Keyword search over text columns.
Hybrid search	Keyword and vector search combined.
Multi-vector search	Several embeddings per document.
Reranking	Reordering results to improve the top-k.
Feature engineering	Deriving columns and tables from data already stored.
Materialized views	A table defined by a query, brought up to date by each refresh.
Training	Reading a table into a data loader.