INDEX_VECTOR_MEMORY

INDEX_VECTOR_MEMORY_ADVISOR

Use the INDEX_VECTOR_MEMORY_ADVISOR procedure to determine the vector memory size needed for a particular vector index. This helps you evaluate the number of indexes that can fit for each simulated vector memory size.

Syntax

Using the number and type of vector dimensions that you want to store in your vector index.

DBMS_VECTOR.INDEX_VECTOR_MEMORY_ADVISOR(
    INDEX_TYPE, 
    NUM_VECTORS, 
    DIM_COUNT, 
    DIM_TYPE, 
    PARAMETER_JSON, 
    RESPONSE_JSON);

Using the table and vector column on which you want to create your vector index:

DBMS_VECTOR.INDEX_VECTOR_MEMORY_ADVISOR(
    TABLE_OWNER, 
    TABLE_NAME, 
    COLUMN_NAME, 
    INDEX_TYPE, 
    PARAMETER_JSON, 
    RESPONSE_JSON);

Table 12-7 Syntax Details: INDEX_VECTOR_MEMORY_ADVISOR

Parameter	Description
`INDEX_TYPE`	Type of vector index: `IVF` for Inverted File Flat (IVF) vector indexes `HNSW` for Hierarchical Navigable Small World (HNSW) vector indexes
`NUM_VECTORS`	Number of vectors that you plan to create the vector index with.
`DIM_COUNT`	Number of dimensions of a vector as a `NUMBER`.
`DIM_TYPE`	Type of dimensions of a vector. Possible values are: `FLOAT16` `FLOAT32` `FLOAT64` `INT8`
`TABLE_OWNER`	Owner name of the table on which to create the vector index.
`TABLE_NAME`	Table name on which to create the vector index.
`COLUMN_NAME`	Name of the vector column on which to create the vector index.
`PARAMETER_JSON`	Input parameter in JSON format. You can specify only one of the following form: `PARAMTER_JSON=>{"accuracy":value}` `INDEX_TYPE=>IVF, parameter_json=>{"neighbor_partitions":value}` `INDEX_TYPE=>HNSW, parameter_json=>{"neighbors":value}` Note: You cannot specify values for `accuracy` along with `neighbor_partitions` or `neighbors`.
`RESPONSE_JSON`	JSON-formatted response string.

Examples

Using neighbors in the parameters list:

exec DBMS_VECTOR.INDEX_VECTOR_MEMORY_ADVISOR(
    INDEX_TYPE=>'HNSW', 
    NUM_VECTORS=>10000, 
    DIM_COUNT=>768, 
    DIM_TYPE=>'FLOAT32', 
    PARAMETER_JSON=>'{"neighbors":128}', 
    RESPONSE_JSON=>:response_json); 

Suggested vector memory pool size: 59918628 Bytes

Using accuracy in the parameters list:

exec DBMS_VECTOR.INDEX_VECTOR_MEMORY_ADVISOR(
    INDEX_TYPE=>'HNSW', 
    NUM_VECTORS=>10000, 
    DIM_COUNT=>768, 
    DIM_TYPE=>'FLOAT32', 
    PARAMETER_JSON=>'{"accuracy":90}', 
    RESPONSE_JSON=>:response_json); 

Suggested vector memory pool size: 53926765 Bytes

Using the table and vector column on which you want to create the vector index:

exec DBMS_VECTOR.INDEX_VECTOR_MEMORY_ADVISOR(
    'VECTOR_USER', 
    'VECTAB', 
    'DATA_VECTOR', 
    'HNSW', 
    RESPONSE_JSON=>:response_json); 

Using default accuracy: 90% 
Suggested vector memory pool size: 76396251 Bytes

Related Topics

Oracle Database AI Vector Search User's Guide

Parent topic: DBMS_VECTOR