This version is still in development and is not considered stable yet. For the latest stable version, please use Spring Data MongoDB 4.4.4!spring-doc.cn

MongoDB Search

MongoDB enables users to do keyword or lexical search as well as vector search data using dedicated search indexes.spring-doc.cn

Vector Search

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.spring-doc.cn

Managing Vector Indexes

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

The following snippet shows how to create a vector index for a collectionspring-doc.cn

Create a Vector Index

Javaspring-doc.cn
Mongo Shellspring-doc.cn

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.spring-doc.cn

Querying Vector Indexes

Vector indexes can be queried by issuing an aggregation using a VectorSearchOperation via MongoOperations as shown in the following examplespring-doc.cn

Query a Vector Index

Javaspring-doc.cn
Mongo Shellspring-doc.cn

VectorSearchOperation search = VectorSearchOperation.search("vector_index") (1)
  .path("plotEmbedding") (2)
  .vector( ... )
  .numCandidates(150)
  .limit(10)
  .quantization(SCALAR)
  .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([
  {
    "$vectorSearch": {
      "index": "vector_index",
      "path": "plot_embedding", (1)
      "queryVector": [ ... ],
      "numCandidates": 150,
      "limit": 10,
      "quantization": "scalar"
    }
  },
  {
    "$addFields": {
      "score": { $meta: "vectorSearchScore" }
    }
  }
])

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