mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-13 15:22:30 +00:00
Jack Ye
70c10e4aa4
lancedb now holds a page at every path the site publishes, including the twelve Enterprise ones. Those are real open-source pages -- what the capability is, and what the embedded form does instead -- rather than blank placeholders, so the open-source site is complete on its own. sophon becomes an overlay whose page replaces the one at the same path, which means navigation lives in one place and the two repositories only have to agree on a path. That removes the whole class of problem where a page could only be reached from one of the two trees. The open-source site went from twenty-four dangling Enterprise links to one, and that one points at the dataset pages the build repository owns. Ports four changes that landed on the docs repository after this branch left it: #344 indexing and quantization rewritten #354 LeRobot native reader #361 training/index renamed to training/data-loading and rewritten #362 distance metric mismatch falls back to a brute-force scan None applied as patches, because these files were merged or split here -- quantization into the vector index page, multi-vector into vector search, performance into four pages. Each was ported by taking the upstream page and re-applying the restructure on top, which also picked up a correction that would otherwise have been lost: the stricter brute-force threshold for multi-vector columns is a Cloud limit, not an Enterprise one. The upstream LeRobot page linked to a Geneva page that no longer exists; it now points at the feature-engineering pages that replaced it.
43 lines
1.9 KiB
Plaintext
43 lines
1.9 KiB
Plaintext
---
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title: "Use cases"
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sidebarTitle: "Overview"
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description: "Worked examples of building with LanceDB, by what you are building."
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---
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Every example 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, refreshed incrementally. |
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| [Training](/training/data-loading) | Reading a table into a data loader. |
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