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MongoDB 検索

MongoDB を使用すると、専用の検索インデックスを使用して、キーワード検索や語彙検索、ベクトル検索データを実行できます。

ベクトル検索

MongoDB Vector Search uses the $vectorSearch aggregation stage to run queries against specialized indexes. Please refer to the MongoDB documentation to learn more about requirements and restrictions of vectorSearch indexes.

Managing Vector Indexes

SearchIndexOperationsProvider implemented by MongoTemplate are the entrypoint to SearchIndexOperations offering various methods for managing vector indexes.

The following snippet shows how to create a vector index for a collection

Create a Vector Index

Java
Mongo シェル

VectorIndex index = new VectorIndex("vector_index")
  .addVector("plotEmbedding"), vector -> vector.dimensions(1536).similarity(COSINE)) (1)
  .addFilter("year"); (2)

mongoTemplate.searchIndexOps(Movie.class) (3)
  .createIndex(index);

1	A vector index may cover multiple vector embeddings that can be added via the `addVector` method.
2	Vector indexes can contain additional fields to narrow down search results when running queries.
3	Obtain `SearchIndexOperations` bound to the `Movie` type which is used for field name mapping.

db.movie.createSearchIndex("movie", "vector_index",
  {
    "fields": [
      {
        "type": "vector",
        "numDimensions": 1536,
        "path": "plot_embedding", (1)
        "similarity": "cosine"
      },
      {
        "type": "filter",
        "path": "year"
      }
    ]
  }
)

1	Field name `plotEmbedding` got mapped to `plot_embedding` considering a `@Field(name = "…")` annotation.

Once created, vector indexes are not immediately ready to use although the exists check returns true. The actual status of a search index can be obtained via SearchIndexOperations#status(…). The READY state indicates the index is ready to accept queries.

Querying Vector Indexes

Vector indexes can be queried by issuing an aggregation using a VectorSearchOperation via MongoOperations as shown in the following example

Query a Vector Index

Java
Mongo シェル

VectorSearchOperation search = VectorSearchOperation.search("vector_index") (1)
  .path("plotEmbedding") (2)
  .vector( ... )
  .numCandidates(150)
  .limit(10)
  .withSearchScore("score"); (3)

AggregationResults<MovieWithSearchScore> results = mongoTemplate
  .aggregate(newAggregation(Movie.class, search), MovieWithSearchScore.class);

1	Provide the name of the vector index to query since a collection may hold multiple ones.
2	The name of the path used for comparison.
3	Optionally add the search score with given name to the result document.

db.embedded_movies.aggregate([
  {
    "$vectorSearch": {
      "index": "vector_index",
      "path": "plot_embedding", (1)
      "queryVector": [ ... ],
      "numCandidates": 150,
      "limit": 10
    }
  },
  {
    "$addFields": {
      "score": { $meta: "vectorSearchScore" }
    }
  }
])

1	Field name `plotEmbedding` got mapped to `plot_embedding` considering a `@Field(name = "…")` annotation.