> ## 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.

# Query-time parameters

> Tune StreamingDiskANN query performance and accuracy dynamically

Configure StreamingDiskANN query behavior at runtime to dynamically tune the accuracy vs performance trade-off for individual queries or sessions.

* Adjust accuracy and speed trade-offs per query
* Fine-tune search quality without rebuilding indexes
* Optimize for different use cases (fast approximate vs high-accuracy search)
* Control rescoring behavior for better accuracy

## Samples

### Increase accuracy for important queries

```sql theme={"dark"}
-- Temporarily increase search list size and rescoring
BEGIN;
SET LOCAL diskann.query_search_list_size = 200;
SET LOCAL diskann.query_rescore = 100;

SELECT * FROM document_embedding
ORDER BY embedding <=> '[...]'
LIMIT 10;

COMMIT;
```

### Fast approximate search

```sql theme={"dark"}
-- Reduce search list size for faster queries
SET diskann.query_search_list_size = 50;
SET diskann.query_rescore = 20;

SELECT * FROM document_embedding
ORDER BY embedding <=> '[...]'
LIMIT 10;
```

### Disable rescoring for maximum speed

```sql theme={"dark"}
-- Use quantized results without rescoring
SET diskann.query_rescore = 0;

SELECT * FROM document_embedding
ORDER BY embedding <=> '[...]'
LIMIT 10;
```

### Session-wide configuration

```sql theme={"dark"}
-- Set parameters for the entire session
SET diskann.query_search_list_size = 150;
SET diskann.query_rescore = 75;

-- All queries in this session use these settings
SELECT * FROM document_embedding ORDER BY embedding <=> '[...]' LIMIT 10;
SELECT * FROM document_embedding ORDER BY embedding <=> '[...]' LIMIT 20;
```

## Parameters

| Parameter                        | Type  | Default | Description                                                                                                     |
| -------------------------------- | ----- | ------- | --------------------------------------------------------------------------------------------------------------- |
| `diskann.query_search_list_size` | `int` | 100     | Number of additional candidates considered during graph search. Higher values improve accuracy but slow queries |
| `diskann.query_rescore`          | `int` | 50      | Number of elements rescored with full precision. Set to 0 to disable rescoring                                  |

### diskann.query\_search\_list\_size

Controls the size of the candidate list during graph traversal:

* **Lower values (20-50)**: Faster queries, reduced accuracy, fewer candidates explored
* **Default (100)**: Balanced performance and accuracy
* **Higher values (100-200)**: Better accuracy, slower queries, more thorough search

This parameter has the most direct impact on the accuracy/speed trade-off.

### diskann.query\_rescore

The number of top candidates to rescore using full-precision vectors:

* **0**: Disable rescoring, use quantized results directly (fastest)
* **Default (50)**: Rescore top 50 candidates for improved accuracy
* **Higher values (50-200)**: More thorough rescoring, better accuracy, slower queries

Rescoring helps correct errors introduced by quantization in memory-optimized indexes. For plain storage indexes, rescoring has minimal effect.

## Setting parameters

### Session-level (persistent)

```sql theme={"dark"}
SET diskann.query_search_list_size = 150;
```

Applies to all queries in the current session until changed or the session ends.

### Transaction-local (temporary)

```sql theme={"dark"}
BEGIN;
SET LOCAL diskann.query_rescore = 100;
-- Parameter applies only within this transaction
SELECT * FROM document_embedding ORDER BY embedding <=> '[...]' LIMIT 10;
COMMIT;
-- Parameter is reset after commit
```

The `LOCAL` keyword ensures parameters reset after the transaction ends.

## Tuning recommendations

### High-accuracy applications

For applications where accuracy is critical (e.g., medical diagnosis, legal document search):

```sql theme={"dark"}
SET diskann.query_search_list_size = 200;
SET diskann.query_rescore = 100;
```

### Real-time applications

For latency-sensitive applications (e.g., chatbots, autocomplete):

```sql theme={"dark"}
SET diskann.query_search_list_size = 50;
SET diskann.query_rescore = 20;
```

### Batch processing

For offline batch processing where throughput matters:

```sql theme={"dark"}
SET diskann.query_search_list_size = 75;
SET diskann.query_rescore = 30;
```

### A/B testing different configurations

```sql theme={"dark"}
-- Test configuration A
BEGIN;
SET LOCAL diskann.query_search_list_size = 100;
SET LOCAL diskann.query_rescore = 50;
EXPLAIN ANALYZE SELECT * FROM document_embedding ORDER BY embedding <=> '[...]' LIMIT 10;
COMMIT;

-- Test configuration B
BEGIN;
SET LOCAL diskann.query_search_list_size = 150;
SET LOCAL diskann.query_rescore = 75;
EXPLAIN ANALYZE SELECT * FROM document_embedding ORDER BY embedding <=> '[...]' LIMIT 10;
COMMIT;
```

### Performance considerations

* Start with default values and adjust `diskann.query_rescore` first
* Use transaction-local settings (`SET LOCAL`) for experimentation
* Monitor query latency with `EXPLAIN ANALYZE` when tuning
* Higher values consume more CPU but not significantly more memory

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

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