indexing_hnsw

Configure HNSW indexing for fast and accurate vector search

Configure indexing using the Hierarchical Navigable Small World (HNSW) algorithm, which is known for fast and accurate approximate nearest neighbor search.

HNSW is suitable for in-memory datasets and scenarios where query speed is crucial.

Samples

SELECT ai.create_vectorizer(
    'blog_posts'::regclass,
    indexing => ai.indexing_hnsw(min_rows => 50000, opclass => 'vector_l1_ops'),
    -- other parameters...
);

Arguments

Name	Type	Default	Required	Description
min_rows	int	100000	✖	The minimum number of rows before creating the index
opclass	text	vector_cosine_ops	✖	The operator class for the index. Possible values are: vector_cosine_ops, vector_l1_ops, or vector_ip_ops
m	int	-	✖	Advanced HNSW parameters
ef_construction	int	-	✖	Advanced HNSW parameters
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.