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 }
    } ] }

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. Here's a sample syntax:

  MongoDB

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  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:

  MongoDB

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  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 } }
    ]
  } )

  SQL

  Copy

  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.

Multikey Indexes (GIN Index)

SingleStore implements multikey indexes as GIN Index. SingleStore recommends using GIN index when the property at the path to search in a BSON document is an array. For more information on GIN indexes, refer to Generalized Inverted Index (GIN) (BSON).

To create a GIN index, use the createIndex command as:

db.<collection>.createIndex({"<path>":"gin"})

where path is the path to a property in the BSON document to index. For example,

db.users.createIndex({"addresses.city":"gin"})

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 } } ]