> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify-poc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# indexing_diskann

> Configure DiskANN indexing for high-performance vector search on large datasets

Configure indexing using the DiskANN algorithm, which is designed for high-performance approximate nearest neighbor search on large-scale datasets. This is suitable for very large datasets that need to be stored on disk.

## Samples

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'blog_posts'::regclass,
    indexing => ai.indexing_diskann(min_rows => 500000, storage_layout => 'memory_optimized'),
    -- other parameters...
);
```

## Arguments

| Name                       | Type    | Default | Required | Description                                                           |
| -------------------------- | ------- | ------- | -------- | --------------------------------------------------------------------- |
| min\_rows                  | int     | 100000  | ✖        | The minimum number of rows before creating the index                  |
| storage\_layout            | text    | -       | ✖        | Set to either `memory_optimized` or `plain`                           |
| num\_neighbors             | int     | -       | ✖        | Advanced [DiskANN][diskann] parameter                                 |
| search\_list\_size         | int     | -       | ✖        | Advanced [DiskANN][diskann] parameter                                 |
| max\_alpha                 | float8  | -       | ✖        | Advanced [DiskANN][diskann] parameter                                 |
| num\_dimensions            | int     | -       | ✖        | Advanced [DiskANN][diskann] parameter                                 |
| num\_bits\_per\_dimension  | int     | -       | ✖        | Advanced [DiskANN][diskann] parameter                                 |
| create\_when\_queue\_empty | boolean | true    | ✖        | Create the index only after all of the embeddings have been generated |

## Returns

A JSON configuration object that you can use in `ai.create_vectorizer`.

[diskann]: https://github.com/microsoft/DiskANN/tree/main