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

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 parameter
search_list_size	int	-	✖	Advanced DiskANN parameter
max_alpha	float8	-	✖	Advanced DiskANN parameter
num_dimensions	int	-	✖	Advanced DiskANN parameter
num_bits_per_dimension	int	-	✖	Advanced 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.