MongoDB Search and MongoDB Vector Search Indexes

MongoDB Search Index Model

To create a MongoDB Search index, you must construct a CreateSearchIndexModel instance that sets your index specifications.

The CreateSearchIndexModel class has the following properties:

The following example creates a CreateSearchIndexModel instance to provide specifications for an index named search_idx. The code specifies static mappings of the title and released fields:

var def = new BsonDocument {
  { "mappings", new BsonDocument {
      { "dynamic", false },
      { "fields", new BsonDocument {
        { "title", new BsonDocument { {"type", "string" } } },
        { "released", new BsonDocument { { "type", "date" } } } } }
  } }
};
var indexModel = new CreateSearchIndexModel(
  "search_idx",
  SearchIndexType.Search,
  def
);

To learn more about MongoDB Search field mappings, see Define Field Mappings in the Atlas documentation.

MongoDB Vector Search Index Model

To create a MongoDB Vector Search index, you must construct a CreateVectorSearchIndexModel instance that sets your index specifications.

The CreateVectorSearchIndexModel class inherits from the CreateSearchIndexModel class and has the following additional properties:

The following example creates a CreateVectorSearchIndexModel instance to provide specifications for an index named vs_idx. The code specifies the embedding path as PlotEmbedding, a class property that corresponds to the plot_embedding field in MongoDB. It also indexes 1536 dimensions, and uses the Euclidean vector similarity function.

var model = new CreateVectorSearchIndexModel<Movie> (
  model => model.PlotEmbedding,
  "vs_idx",
  VectorSimilarity.Euclidean,
  1536);

To learn more about defining MongoDB Vector Search indexes, see How to Index Fields for Vector Search in the Atlas documentation.

MongoDB Auto-Embedding Search Index Model

You can use a CreateAutoEmbeddingVectorSearchIndexModel index model to create a MongoDB Vector Search index that automatically generates vector embeddings for text fields.

The CreateAutoEmbeddingVectorSearchIndexModel has the following properties, in addition to the properties inherited from CreateVectorSearchIndexModelBase<TDocument>:

The following example creates a CreateAutoEmbeddingVectorSearchIndexModel instance that provides specifications for an index named auto_embedded_index. This index uses the "voyage-4" embedding model to automatically generate vector embeddings for the plot field, and also includes optional filters for the runtime and year fields:

var model = new CreateAutoEmbeddingVectorSearchIndexModel<EmbeddedMovie>(
  m => m.Plot,
  "auto_embedding_index",
  "voyage-4",
  m => m.Runtime, m => m.Year  // Optional filter fields
);

Example

The following example creates a MongoDB Search index on the embedded_movies collection. The code creates a CreateSearchIndexModel that sets the index name and enables dynamic mapping. Then, the code passes the CreateSearchIndexModel instance to the SearchIndexes.CreateOne() method to create the MongoDB Search index:

var indexModel = new CreateSearchIndexModel(
  "example_index",
  SearchIndexType.Search,
  new BsonDocument {
    { "mappings", new BsonDocument {
      { "dynamic", true },
    } }
  }
);
var result = movieCollection.SearchIndexes.CreateOne(indexModel);
Console.WriteLine("Created MongoDB Search index:\n{0}", result);

Created MongoDB Search index:
"example_index"

Example

This example performs the following actions:

1. Creates a CreateSearchIndexModel instance that specifies a MongoDB Search index named as_idx

2. Creates a CreateVectorSearchIndexModel instance that specifies a MongoDB Vector Search index named vs_idx

3. Passes a List of the CreateSearchIndexModel and CreateVectorSearchIndexModel instances to the SearchIndexes.CreateMany() method

4. Creates the MongoDB Search and MongoDB Vector Search indexes on the embedded_movies collection

var searchModel = new CreateSearchIndexModel(
  "as_idx",
  SearchIndexType.Search,
  new BsonDocument {
    { "mappings", new BsonDocument {
      { "dynamic", true },
    } }
  }
);
var vectorModel = new CreateVectorSearchIndexModel<Movie>(
  m => m.PlotEmbedding,
  "vs_idx",
  VectorSimilarity.Euclidean,
  1536);
var models = new List<CreateSearchIndexModel> { searchModel, vectorModel };
var indexes = movieCollection.SearchIndexes.CreateMany(models);
Console.WriteLine("Created Search indexes:\n{0} {1}", indexes.ToArray());

Created Search indexes:
as_idx vs_idx

Example

The following example accesses information about the MongoDB Search and MongoDB Vector Search indexes created in the Create Multiple Search Indexes section of this page. The code calls the SearchIndexes.List() method and prints a list of the MongoDB Search and MongoDB Vector Search indexes on the collection:

var indexesList = movieCollection.SearchIndexes.List().ToList();
foreach (var i in indexesList)
{
  Console.WriteLine(i);
}

{ "id": "...", "name": "as_idx", "status": "READY", "queryable":
true, "latestDefinitionVersion": {...}, "latestDefinition": {
"mappings": { "dynamic": true } }, "statusDetail": [...] }
{ "id": "...", "name": "vs_idx", "type": "vectorSearch", "status":
"READY", "queryable": true, ..., "latestDefinition": { "fields": [{
"type": "vector", "path": "plot_embedding", "numDimensions": 1536,
"similarity": "euclidean" }] }, "statusDetail": [...] }

Example

The following example updates the Vector Search index named vs_index created in the Create Multiple Search Indexes section of this page. The code creates a new index definition document that instructs the index to use "dotProduct" as the vector similarity function. Then, the code calls the SearchIndexes.Update() method to update the index:

var updatedDef = new BsonDocument
{
    { "fields", new BsonArray
        {
            new BsonDocument
            {
                { "type", "vector" },
                { "path", "plot_embedding" },
                { "numDimensions", 1536 },
                { "similarity", "dotProduct" }
            }
        }
    }
};
movieCollection.SearchIndexes.Update("vs_index", updatedDef);

Example

The following example deletes the MongoDB Search index named example_index created in the Create a Search Index section of this page. The code passes the index name to the SearchIndexes.DropOne() method to delete the index:

movieCollection.SearchIndexes.DropOne("example_index");

API Documentation

To learn more about the methods and types mentioned in this guide, see the following API documentation:

• CreateSearchIndexModel

• SearchIndexType

• CreateOne()

• CreateMany()

• List()

• Update()

• DropOne()