createIndexes

Creates indexes on MongoDB® collections. To create vector indexes, use the kaiIndexOptions parameter. Refer to Vector Indexing for information on vector indexes supported in SingleStore. This command is an extension of the MongoDB® createIndexes command.

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

createIndexes

String

Required

The collection for which the index is created.

indexes

JSON array

Required

An array that specifies the index to create. Each document in the array specifies a separate index.

name

String

Required

A name for the vector index.

kaiIndexOptions

JSON object

Required

Specifies index options for the vector index.

Remarks

  • SingleStore recommends creating collections (or tables) before creating a vector index. Creating vector indexes on existing collections is not supported. You must specify the field that holds the vector embeddings while creating the collection. 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_type and dimensions fields in the kaiIndexOptions 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.<collection_name>.getIndexes() command to view existing indexes on a collection. For example:

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: June 20, 2024

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