> ## 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.

# CREATE INDEX with diskann

> Create a StreamingDiskANN index for high-performance vector search

export const PG = 'Postgres';

Create a StreamingDiskANN index on a vector column for high-performance similarity search with optional label-based filtering.

* Create indexes for fast approximate nearest neighbor search
* Configure index build-time parameters for optimal performance
* Enable label-based filtering for precise vector search
* Choose storage layouts for memory optimization or plain storage

Note that:

* The index build process can be memory-intensive. Consider increasing `maintenance_work_mem`:
  ```sql theme={"dark"}
  SET maintenance_work_mem = '2GB';
  ```

* Label values must be within {PG} `smallint` range (-32768 to 32767)

* Creating indexes on UNLOGGED tables is not currently supported

* Null vectors are not indexed; null labels are treated as empty arrays

## Samples

### Basic index creation

```sql theme={"dark"}
CREATE INDEX document_embedding_idx ON document_embedding
USING diskann (embedding vector_cosine_ops);
```

### With custom parameters

```sql theme={"dark"}
CREATE INDEX document_embedding_idx ON document_embedding
USING diskann (embedding vector_cosine_ops)
WITH (num_neighbors = 50, search_list_size = 100);
```

### With label-based filtering

```sql theme={"dark"}
CREATE INDEX document_embedding_idx ON document_embedding
USING diskann (embedding vector_cosine_ops, labels);
```

### With storage layout

```sql theme={"dark"}
CREATE INDEX document_embedding_idx ON document_embedding
USING diskann (embedding vector_cosine_ops)
WITH (storage_layout = 'memory_optimized');
```

## Syntax

```sql theme={"dark"}
CREATE INDEX index_name ON table_name
USING diskann (embedding_column distance_ops [, labels_column])
[WITH (parameter = value, ...)];
```

## Distance operators

| Operator            | Description             | Use with                                        |
| ------------------- | ----------------------- | ----------------------------------------------- |
| `vector_cosine_ops` | Cosine distance (`<=>`) | Normalized embeddings                           |
| `vector_l2_ops`     | L2 distance (`<->`)     | Euclidean distance                              |
| `vector_ip_ops`     | Inner product (`<#>`)   | Dot product (not compatible with plain storage) |

## Build-time parameters

| Parameter                | Type    | Default                                | Description                                                                                     |
| ------------------------ | ------- | -------------------------------------- | ----------------------------------------------------------------------------------------------- |
| `storage_layout`         | `text`  | `memory_optimized`                     | `memory_optimized` uses Statistical Binary Quantization; `plain` stores uncompressed data       |
| `num_neighbors`          | `int`   | 50                                     | Maximum number of neighbors per node. Higher values increase accuracy but slow graph traversal  |
| `search_list_size`       | `int`   | 100                                    | Search parameter during construction. Higher values improve graph quality but slow index builds |
| `max_alpha`              | `float` | 1.2                                    | Alpha parameter in the algorithm. Higher values improve graph quality but slow index builds     |
| `num_dimensions`         | `int`   | 0 (all dimensions)                     | Number of dimensions to index. Useful for Matryoshka embeddings                                 |
| `num_bits_per_dimension` | `int`   | 2 (if less than 900 dims), 1 otherwise | Bits per dimension when using Statistical Binary Quantization                                   |

For detailed information about each parameter and tuning recommendations, see [Index build-time parameters][index_parameters].

To configure query behavior at runtime, see [Query-time parameters][query_parameters].

[index_parameters]: /api-reference/pgvectorscale/index_parameters

[query_parameters]: /api-reference/pgvectorscale/query_parameters