mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-14 15:52:26 +00:00
Brendan Clement
622e6078c1
Main now rebuilds a materialized view completely on every refresh (#4373): there is no incremental mode, no `full` argument and no stable-row-ID requirement on the source. The example ran against the SDK built from this branch (0.40.0b13) failed, because a refresh after adding Bea writes both matching rows, not one. The example now creates its source with the default options and expects two rows written by the refresh, and its generated snippet is regenerated. Separate tests cover `with_no_data` (one matching row on the first refresh) and refreshing to an earlier source version. The refresh page explains the full rebuild and keeps the #incremental and #stable-row-ids anchors for the sections that replace them. Its version note now says what the published 0.39.0 does differently, as observed with that release. The management, table, database, limits, architecture and data-types pages no longer say a view's source needs stable row ids, and the feature-engineering and tutorial summaries no longer call refreshes incremental.
43 lines
1.9 KiB
Plaintext
43 lines
1.9 KiB
Plaintext
---
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title: "Tutorials"
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sidebarTitle: "Overview"
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description: "Worked examples of building with LanceDB, grouped by what they build."
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---
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Every tutorial is one page in the sidebar, grouped by what it builds. The
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documentation explains how a feature works; these show it doing something.
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Examples marked as notebooks live in the
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[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository and
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are runnable in Colab; the rest are written up here.
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## Where to start {#start}
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<CardGroup cols={2}>
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<Card title="Search" icon="magnifying-glass" href="/tutorials/search/hybrid-search-beir">
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Vector, multi-vector, hybrid and full-text retrieval.
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</Card>
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<Card title="RAG" icon="robot" href="/tutorials/agents/contextual-rag">
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Retrieval-augmented generation and agent applications.
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</Card>
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<Card title="Feature engineering" icon="wand-magic-sparkles" href="/tutorials/feature-engineering/feature-engineering-101">
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Deriving columns, materialized views, experimenting on branches.
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</Card>
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<Card title="Training" icon="graduation-cap" href="/training/object-detection">
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Curating data and training models directly on a table.
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</Card>
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</CardGroup>
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## The features these use {#read-the-docs}
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| Feature | What it covers |
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|:--------|:------------|
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| [Vector search](/search/vector-search) | Similarity search, distance metrics, and tuning it. |
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| [Full-text search](/search/full-text-search) | Keyword search over text columns. |
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| [Hybrid search](/search/hybrid-search) | Keyword and vector search combined. |
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| [Multi-vector search](/search/vector-search) | Several embeddings per document. |
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| [Reranking](/reranking) | Reordering results to improve the top-k. |
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| [Feature engineering](/feature-engineering) | Deriving columns and tables from data already stored. |
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| [Materialized views](/management/materialized-view) | A table defined by a query, recomputed by each refresh. |
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| [Training](/training/data-loading) | Reading a table into a data loader. |
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