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createSearchIndexes (database command)

Definition

createSearchIndexes

New in version 7.0: (Also available starting in 6.0.7)

Creates one or more MongoDB Search indexes or Vector Search indexes on a specified collection or view.

The mongosh method db.collection.createSearchIndex() provides a wrapper around the createSearchIndexes database command.

Important

This command can only be run on a deployment hosted on MongoDB Atlas.

Syntax

Command syntax:

db.runCommand(
   {
      createSearchIndexes: "<collection name>",
      indexes: [
         {
            name: "<index name>",
            type: "<search index type>",
            definition: {
               /* search index definition fields */
            }
        },
        ...
      ]
   }
)

Command Fields

The createSearchIndexes command takes the following fields:

Field	Type	Necessity	Description
`createSearchIndexes`	string	Required	Name of the collection on which to create the search index.
`indexes`	array	Required	Array of documents describing the indexes to create.
`indexes.name`	string	Optional	Name of the search index to create. You cannot create multiple indexes with the same name on a single collection. If you do not specify a `name`, the index is named `default`.
`indexes.type`	string	Optional	Type of search index to create. You can specify either: `search` `vectorSearch` If you omit the `type` field, the index type is `search`.
`indexes.definition`	document	Required	Document describing the index to create. The `definition` syntax depends on whether you create a standard search index or a Vector Search index. For the `definition` syntax, see: Search Index Definition Syntax Vector Search Index Definition Syntax

Search Index Definition Syntax

The search index definition takes the following fields:

{
   analyzer: "<analyzer-for-index>",
   searchAnalyzer: "<analyzer-for-query>",
   mappings: {
      dynamic: <boolean>,
      fields: { <field-definition> }
   },
   analyzers: [ <custom-analyzer> ],
   storedSource: <boolean> | {
      <stored-source-definition>
   },
   synonyms: [ {
      name: "<synonym-mapping-name>",
      source: {
         collection: "<source-collection-name>"
      },
      analyzer: "<synonym-mapping-analyzer>"
   } ]
}

Field	Type	Necessity	Description
`analyzer`	string	Optional	Specifies the analyzer to apply to string fields when indexing. If you omit this field, the index uses the standard analyzer.
`searchAnalyzer`	string	Optional	Specifies the analyzer to apply to query text before the text is searched. If you omit this field, the index uses the same analyzer specified in the `analyzer` field. If you omit both the `searchAnalyzer` and the `analyzer` fields, the index uses the standard analyzer.
`mappings`	object	Required	Specifies how to index fields on different paths for this index.
`mappings.dynamic`	boolean	Optional	Enables or disables dynamic field mapping for this index. If set to `true`, the index contains all fields containing supported data types. If set to `false`, you must specify individual fields to index using `mappings.fields`. If omitted, defaults to `false`.
`mappings.fields`	document	Conditional	Required only if dynamic mapping is disabled. Specifies the fields to index. To learn more, see Define Field Mappings.
`analyzers`	array	Optional	Specifies the Custom Analyzers to use in this index.
`storedSource`	boolean or Stored Source Definition	Optional	Specifies document fields to store for queries performed using the returnedStoredSource option. You can store fields of all MongoDB Search Field Types on MongoDB Search. The `storedSource` value can be one of these: `true`, to store all fields `false`, to not store any fields An object that specifies the fields to `include` or `exclude` from storage If omitted, defaults to `false`. To learn more, see Define Stored Source Fields in Your MongoDB Search Index.
`synonyms`	array of Synonym Mapping Definitions	Optional	Specifies synonym mappings to use in your index. Configuring synonyms allows you to index and search for words that have the same or a similar meaning. To learn more, see Define Synonym Mappings in Your MongoDB Search Index.

Vector Search Index Definition Syntax

The vector search index definition takes the following fields:

{
  "fields": [
    {
      "type": "vector" | "filter",
      "path": "<field-to-index>",
      "numDimensions": <number-of-dimensions>,
      "similarity": "euclidean" | "cosine" | "dotProduct"
    }
  ]
}

For explanations of vector search index definition fields, see How to Index Fields for Vector Search.

Behavior

The createSearchIndexes command triggers an index build. There may be a delay between when you receive a response from the command and when the index is ready.

To see the status of your search indexes, use the $listSearchIndexes aggregation stage.

Access Control

If your deployment enforces access control, the user running the createSearchIndexes command must have the createSearchIndexes privilege action on the database or collection:

{
   resource: {
      db : <database>,
      collection: <collection>
   },
   actions: [ "createSearchIndexes" ]
}

The built-in readWrite role provides the createSearchIndexes privilege. The following example grants accountUser01 the readWrite role on the products database:

db.grantRolesToUser(
   "accountUser01",
   [ { role: "readWrite", db: "products" } ]
)

Output

The createSearchIndexes command output resembles the following:

{
   ok: 1,
   indexesCreated: [
      {
         id: "<index Id>",
         name: "<index name>"
      }
   ]
}

Important

The response field ok: 1 indicates that the command was successful. However, there may be a delay between when you receive the response and when the created indexes are ready for use.

To see the status of your search indexes, use the $listSearchIndexes aggregation stage.

Examples

Create a Search Index on All Fields

The following example creates a search index named searchIndex01 on the contacts collection:

db.runCommand( {
   createSearchIndexes: "contacts",
   indexes: [
      {
         name: "searchIndex01",
         definition: { mappings: { dynamic: true } }
      }
   ]
} )

The index definition specifies mappings: { dynamic: true }, which means that the index contains all fields in the collection that have supported data types.

Create a Search Index with a Language Analyzer

A language analyzer introduces stop-words, which are words that are not significant enough to be indexed.

The following example creates a search index named frenchIndex01 on the cars collection, and specifies the lucene.french analyzer on the fr field:

db.runCommand( {
   createSearchIndexes: "cars",
   indexes: [
      {
         name: "frenchIndex01",
         definition: {
            mappings: {
              fields: {
                subject: {
                  fields: {
                    fr: {
                      analyzer: "lucene.french",
                      type: "string"
                    }
                  },
                  type: "document"
                }
              }
            }
          }
      }
   ]
} )

To learn more about language analyzers, see Language Analyzers.

Create Multiple Search Indexes

The following command creates two search indexes on the products collection, searchIndex02 and searchIndex03:

db.runCommand( {
   createSearchIndexes: "products",
   indexes: [
      {
         name: "searchIndex02",
         definition: {
            mappings: {
               fields: {
                  title: {
                     type: "string",
                     analyzer: "lucene.simple"
                  }
               }
            }
         }
      },
      {
         name: "searchIndex03",
         definition:
            {
               mappings: { dynamic: true }
            }
      }
   ]
} )

searchIndex02 uses a simple analyzer on the title field. The simple analyzer divides text into searchable terms based on non-letter characters, such as whitespace, punctuation, or digits.

searchIndex03 uses a dynamic field mapping, meaning the index contains all fields in the collection that have supported data types.

Create a Vector Search Index

The following example creates a vector search index named vectorSearchIndex01 on the movies collection:

db.runCommand( {
   createSearchIndexes: "movies",
   indexes: [
      {
         name: "vectorSearchIndex01",
         type: "vectorSearch",
         definition: {
            fields: [
               {
                  type: "vector",
                  numDimensions: 1,
                  path: "genre",
                  similarity: "cosine"
               }
            ]
         }
      }
   ]
} )

The vector search index contains one dimension and indexes the genre field.

Learn More

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