Vector index

Vector index in row-oriented tables is created using the same syntax as secondary indexes, by specifying vector_kmeans_tree as the index type. Subset of syntax available for vector indexes:

CREATE TABLE `<table_name>` (
    ...
    INDEX `<index_name>`
        GLOBAL
        [SYNC]
        USING vector_kmeans_tree
        ON ( <index_columns> )
        [COVER ( <cover_columns> )]
        [WITH ( <parameter_name> = <parameter_value>[, ...])]
    [,   ...]
)

Where:

• <index_name> - unique index name for data access
• SYNC - indicates synchronous data writing to the index. This is the only currently available option, and it is used by default.
• <index_columns> - comma-separated list of table columns used for index searches (the last column is used as embedding, others as filtering columns)
• <cover_columns> - list of additional table columns stored in the index to enable retrieval without accessing the main table
• <parameter_name> and <parameter_value> - list of key-value parameters:

• common parameters for all vector indexes:
  • vector_dimension - embedding vector dimensionality (should be between 1 and 16384)
  • vector_type - vector value type (float, uint8, or int8)
  • distance - distance function (cosine, manhattan, or euclidean), mutually exclusive with similarity
    • similarity - similarity function (inner_product or cosine), mutually exclusive with distance
• specific parameters for vector_kmeans_tree (see the reference):
  • clusters - number of centroids for k-means algorithm (should be between 2 and 2048)
  • levels - number of levels in the tree (should be between 1 and 16)
  • the total number of nodes in the tree, calculated as clusters raised to the power of levels, should be no more than 1073741824
  • the product of vector_dimension and clusters should be no more than 4194304

Example

CREATE TABLE user_articles (
    article_id Uint64,
    user String,
    title String,
    text String,
    embedding String,
    INDEX emb_cosine_idx GLOBAL SYNC USING vector_kmeans_tree
    ON (user, embedding) COVER (title, text)
    WITH (
        distance="cosine",
        vector_type="float",
        vector_dimension=512,
        clusters=128,
        levels=2
    ),
    PRIMARY KEY (article_id)
)