---
title: "Tutorials"
sidebarTitle: "Overview"
description: "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](https://github.com/lancedb/vectordb-recipes) repository and
are runnable in Colab; the rest are written up here.

## Where to start {#start}

<CardGroup cols={2}>
  <Card title="Search" icon="magnifying-glass" href="/tutorials/search/hybrid-search-beir">
    Vector, multi-vector, hybrid and full-text retrieval.
  </Card>
  <Card title="RAG" icon="robot" href="/tutorials/agents/contextual-rag">
    Retrieval-augmented generation and agent applications.
  </Card>
  <Card title="Feature engineering" icon="wand-magic-sparkles" href="/tutorials/feature-engineering/feature-engineering-101">
    Deriving columns, materialized views, experimenting on branches.
  </Card>
  <Card title="Training" icon="graduation-cap" href="/training/object-detection">
    Curating data and training models directly on a table.
  </Card>
</CardGroup>

## The features these use {#read-the-docs}

| Feature | What it covers |
|:--------|:------------|
| [Vector search](/search/vector-search) | Similarity search, distance metrics, and tuning it. |
| [Full-text search](/search/full-text-search) | Keyword search over text columns. |
| [Hybrid search](/search/hybrid-search) | Keyword and vector search combined. |
| [Multi-vector search](/search/vector-search) | Several embeddings per document. |
| [Reranking](/reranking) | Reordering results to improve the top-k. |
| [Feature engineering](/feature-engineering) | Deriving columns and tables from data already stored. |
| [Materialized views](/management/materialized-view) | A table defined by a query, brought up to date by each refresh. |
| [Training](/training/data-loading) | Reading a table into a data loader. |