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MkDocs validation and Export internal linking (#10368)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-04-27 21:33:23 +02:00 • committed by GitHub
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5
docs/en/datasets/explorer/api.md
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@ -53,7 +53,8 @@ dataframe = explorer.get_similar(idx=0)
Embeddings table for a given dataset and model pair is only created once and reused. These use [LanceDB](https://lancedb.github.io/lancedb/) under the hood, which scales on-disk, so you can create and reuse embeddings for large datasets like COCO without running out of memory.
In case you want to force update the embeddings table, you can pass `force=True` to `create_embeddings_table` method.
You can directly access the LanceDB table object to perform advanced analysis. Learn more about it in [Working with table section](#4-advanced---working-with-embeddings-table)
You can directly access the LanceDB table object to perform advanced analysis. Learn more about it in the [Working with Embeddings Table section](#4-working-with-embeddings-table)
## 1. Similarity Search
@ -196,7 +197,7 @@ You can also plot the results of a SQL query using the `plot_sql_query` method.
exp.plot_sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%' LIMIT 10")
```
## 4. Advanced - Working with Embeddings Table
## 4. Working with Embeddings Table
You can also work with the embeddings table directly. Once the embeddings table is created, you can access it using the `Explorer.table`