MongoDB Search and MongoDB Vector Search Indexes

개요

In this guide, you can learn how to create and manage MongoDB Search and MongoDB Vector Search indexes. These indexes allow you to use the following features:

• MongoDB Search: Perform fast, full-text searches

• MongoDB Vector Search: Perform semantic (similarity) searches on vector embeddings

MongoDB Search and MongoDB Vector Search indexes specify which fields to index, specify how these fields are indexed, and set other optional configurations.

This guide explains how to perform the following actions to manage your MongoDB Search and MongoDB Vector Search indexes:

• 검색 인덱스 모델 만들기

• 검색 인덱스 만들기

• 다중 검색 인덱스 만들기

• 검색 인덱스 나열

• 검색 인덱스 업데이트

• 검색 인덱스 삭제

검색 인덱스 모델 만들기

To create a MongoDB Search or MongoDB Vector Search index, you must first build a CreateSearchIndexModel instance that sets your index specifications.

CreateSearchIndexModel 에는 다음과 같은 속성이 있습니다.

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

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

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
);

var def = new BsonDocument
{
    { "fields", new BsonArray
        {
            new BsonDocument
            {
                { "type", "vector" },
                { "path", "plot_embedding" },
                { "numDimensions", 1536 },
                { "similarity", "euclidean" }
            }
        }
    }
};
var indexModel = new CreateSearchIndexModel(
  "vs_idx",
  SearchIndexType.VectorSearch,
  def
);

검색 인덱스 만들기

You can create a MongoDB Search or MongoDB Vector Search index on a collection by calling the SearchIndexes.CreateOne() method on an IMongoCollection instance. This method accepts an index model as a parameter, specified in a CreateSearchIndexModel instance.

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"

다중 검색 인덱스 만들기

You can create multiple MongoDB Search and MongoDB Vector Search indexes by calling the SearchIndexes.CreateMany() method on an IMongoCollection instance. This method accepts an IEnumerable of CreateSearchIndexModel instances as a parameter.

var searchModel = new CreateSearchIndexModel(
  "as_idx",
  SearchIndexType.Search,
  new BsonDocument {
    { "mappings", new BsonDocument {
      { "dynamic", true },
    } }
  }
);
var vectorModel = new CreateSearchIndexModel(
  "vs_idx",
  SearchIndexType.VectorSearch,
  new BsonDocument
  {
      { "fields", new BsonArray
          {
              new BsonDocument
              {
                  { "type", "vector" },
                  { "path", "plot_embedding" },
                  { "numDimensions", 1536 },
                  { "similarity", "euclidean" }
              }
          }
      }
  }
);
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

검색 인덱스 나열

You can access information about a collection's existing MongoDB Search and MongoDB Vector Search indexes by calling the SearchIndexes.List() method 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": [...] }

검색 인덱스 업데이트

You can update a MongoDB Search or MongoDB Vector Search index by calling the SearchIndexes.Update() method on an IMongoCollection instance. This method accepts the following parameters:

• 업데이트할 인덱스의 이름

• 수정된 인덱스 정의 문서

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

검색 인덱스 삭제

You can delete a MongoDB Search or MongoDB Vector Search index by calling the SearchIndexes.DropOne() method on an IMongoCollection instance. This method accepts the name of the index to delete as a parameter.

movieCollection.SearchIndexes.DropOne("example_index");

추가 정보

.NET/ C# 드라이버 사용하여 만들 수 있는 다른 인덱스에 대해 학습하려면 인덱스 만들기 및 관리 가이드를 참조하세요.

To learn more about MongoDB Search, see the following Atlas documentation:

• MongoDB Search Best Practices

• Tune MongoDB Search Performance

• MongoDB Search M0, M2, and M5 Limitations

• FAQ: MongoDB 검색

To learn more about MongoDB Vector Search, see the following Atlas documentation:

• 벡터 검색 성능 개선하기

• MongoDB Vector Search 빠른 시작

• 쿼리 결과의 정확성을 측정하는 방법