> ## 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.

# Index build-time parameters

> Configure StreamingDiskANN index build-time behavior for optimal performance

export const COMPANY = 'Tiger Data ';

Configure StreamingDiskANN index behavior at build time to optimize for your specific workload, accuracy requirements, and resource constraints.

* Control index build performance and memory usage
* Tune accuracy vs performance trade-offs
* Configure storage layout and compression
* Enable Matryoshka embedding support

## Samples

### Memory-optimized storage with custom neighbors

```sql theme={"dark"}
CREATE INDEX document_embedding_idx ON document_embedding
USING diskann (embedding vector_cosine_ops)
WITH (
    storage_layout = 'memory_optimized',
    num_neighbors = 75
);
```

### Plain storage for maximum accuracy

```sql theme={"dark"}
CREATE INDEX document_embedding_idx ON document_embedding
USING diskann (embedding vector_cosine_ops)
WITH (
    storage_layout = 'plain',
    num_neighbors = 100,
    search_list_size = 200
);
```

### Matryoshka embeddings

```sql theme={"dark"}
-- Index only first 256 dimensions of a 1536-dimension embedding
CREATE INDEX document_embedding_idx ON document_embedding
USING diskann (embedding vector_cosine_ops)
WITH (num_dimensions = 256);
```

### High-accuracy build configuration

```sql theme={"dark"}
CREATE INDEX document_embedding_idx ON document_embedding
USING diskann (embedding vector_cosine_ops)
WITH (
    num_neighbors = 100,
    search_list_size = 200,
    max_alpha = 1.5
);
```

### Build performance considerations

Index builds can be memory-intensive. To improve build performance:

```sql theme={"dark"}
-- Increase maintenance work memory
SET maintenance_work_mem = '2GB';

-- Then create the index
CREATE INDEX document_embedding_idx ON document_embedding
USING diskann (embedding vector_cosine_ops)
WITH (num_neighbors = 50);
```

The default `maintenance_work_mem` is typically 64MB, which may be too low for building indexes on large datasets.

## Parameters

| Parameter                | Type    | Default                                | Description                                                                                                          |
| ------------------------ | ------- | -------------------------------------- | -------------------------------------------------------------------------------------------------------------------- |
| `storage_layout`         | `text`  | `memory_optimized`                     | Storage format: `memory_optimized` uses Statistical Binary Quantization; `plain` stores uncompressed vectors         |
| `num_neighbors`          | `int`   | 50                                     | Maximum number of neighbors per node. Higher values increase accuracy but slow graph traversal                       |
| `search_list_size`       | `int`   | 100                                    | Search list size during construction (S parameter). Higher values improve graph quality at the cost of slower builds |
| `max_alpha`              | `float` | 1.2                                    | Alpha parameter in the DiskANN algorithm. Higher values improve graph quality but slow index builds                  |
| `num_dimensions`         | `int`   | 0 (all dimensions)                     | Number of dimensions to index. Enables Matryoshka embeddings by indexing fewer dimensions                            |
| `num_bits_per_dimension` | `int`   | 2 (if less than 900 dims), 1 otherwise | Bits per dimension for Statistical Binary Quantization encoding                                                      |

### storage\_layout

Controls how vector data is stored:

* **memory\_optimized** (default): Uses Statistical Binary Quantization (SBQ) developed by {COMPANY} researchers to compress vectors. Provides excellent performance with reduced memory footprint.

* **plain**: Stores vectors uncompressed. Uses more memory but may provide slightly higher accuracy. Required for certain distance operators.

### num\_neighbors

The maximum number of neighbors per node in the graph. This is a key parameter for balancing accuracy and performance:

* Lower values (20-50): Faster queries, lower memory, slightly reduced accuracy
* Higher values (50-100): Better accuracy, slower queries, more memory

### search\_list\_size

The size of the candidate list during graph construction (S parameter in DiskANN). Affects index build quality:

* Lower values (50-100): Faster index builds, slightly lower quality
* Higher values (100-200): Slower builds, higher quality graph structure

### max\_alpha

The alpha parameter controls pruning during graph construction:

* Lower values (1.0-1.2): More aggressive pruning, faster builds
* Higher values (1.2-2.0): Less aggressive pruning, higher quality graph

### num\_dimensions

Enables Matryoshka embedding support by indexing only the first N dimensions:

* 0 (default): Index all dimensions
* Positive integer: Index only first N dimensions

Useful for embeddings trained with Matryoshka representation learning, where information is organized hierarchically across dimensions.

### num\_bits\_per\_dimension

Controls the precision of Statistical Binary Quantization:

* 1 bit: Maximum compression, fastest queries, lower accuracy
* 2 bits: Balanced compression and accuracy (default for \<900 dimensions)

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

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