createIndexes
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Creates indexes on MongoDB® collections.kaiIndexOptions
parameter.
Syntax
{"createIndexes": "<collection_name>","indexes": [{ "name": "<index_name>","key": { "<path_to_property>": "vector" },"kaiIndexOptions": {"index_type": "string""dimensions": <integer_value>,// other fields relevant to each index type }} ] }
Arguments
Field |
Type |
Requirement |
Description |
---|---|---|---|
|
String |
Required |
The collection for which the index is created. |
|
JSON array |
Required |
An array that specifies the index to create. |
|
String |
Required |
A name for the vector index. |
|
JSON object |
Required |
Specifies index options for the vector index. |
Remarks
-
The
createIndexes
command creates an index on the specified field in a collection using a persisted computed column within the same collection. -
SingleStore recommends creating collections (or tables) before creating a vector index.
You must specify the field that holds the vector embeddings while creating the collection. Creating vector indexes on existing collections is not supported. Here's a sample syntax: db.createCollection("<collection_name>",columns= [{'id': '<vector_field_name>', 'type': "VECTOR(<vector_dimension>, <type>) NOT NULL"}])Refer to createCollection for more information.
-
If the specified collection does not exist, it is created along with the required structures.
For example: db.runCommand({createIndexes: "sampleCollection",indexes: [{ key: { "sampleCollection.vectorField": "vector" },name: "searchVector_IVF_EUC",kaiIndexOptions:{ "index_type":"IVF_FLAT","nlist":5,"nprobe":1,"metric_type": "EUCLIDEAN_DISTANCE","dimensions": 2 } }]} )DESC sampleCollection;+-------------------------------+----------------+------+-----+---------+----------+ | Field | Type | Null | Key | Default | Extra | +-------------------------------+----------------+------+-----+---------+----------+ | _id | bson | NO | | NULL | | | _more | bson | NO | | NULL | | | $_id | longblob | YES | UNI | NULL | computed | | $sampleCollection.vectorField | vector(2, F32) | NO | MUL | NULL | computed | +-------------------------------+----------------+------+-----+---------+----------+
-
Use the
index_
andtype dimensions
fields in thekaiIndexOptions
parameter to specify the type of vector index and the dimension of the vector, respectively. -
You can specify supported Index Options in the
kaiIndexOptions
parameter for each index type.The specified values are set as defaults for the respective index search parameters.
Example
The following example shows how to create a vector index using the createIndexes
command.
db.runCommand({createIndexes: "exampleCollection",indexes: [{ key: { "exampleCollection.vectorField": "vector" },name: "searchVector_IVF_EUC",kaiIndexOptions:{ "index_type":"IVF_FLAT","nlist":5,"nprobe":1,"metric_type": "EUCLIDEAN_DISTANCE","dimensions": 2 } }]} )
Use the db.
command to view existing indexes on a collection.
db.exampleCollection.getIndexes()
[ { v: 2, key: { _id: 1 }, name: '_id_' },
{ v: 2,
key: { 'exampleCollection.vectorField': 'vector' },
name: 'searchVector_IVF_EUC',
kaiSearchSpec:
{ nlist: 5,
nprobe: 1,
index_type: 'IVF_FLAT',
metric_type: 'EUCLIDEAN_DISTANCE',
dimensions: 2 } } ]
References
Last modified: October 17, 2024