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DOCSP-43826: v5.2 release (#574)
* DOCSP-43826: v5.2 release * link fix and trim table * trim * trim
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‎config/redirects

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define: prefix docs/drivers/java/sync
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define: base https://www.mongodb.com/${prefix}
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define: versions v4.3 v4.4 v4.5 v4.6 v4.7 v4.8 v4.9 v4.10 v4.11 v5.0 v5.1 master
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define: versions v4.3 v4.4 v4.5 v4.6 v4.7 v4.8 v4.9 v4.10 v4.11 v5.0 v5.1 v5.2 master
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raw: ${prefix}/ -> ${base}/current/
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raw: ${prefix}/master -> ${base}/upcoming/

‎snooty.toml

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driver = "java"
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driver-long = "MongoDB Java Driver"
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driver-short = "Java driver"
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version = "5.1"
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full-version = "{+version+}.4"
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version = "5.2"
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full-version = "{+version+}.0"
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mdb-server = "MongoDB Server"
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package-name-org = "mongodb-org"
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api = "https://mongodb.github.io/mongo-java-driver/{+version+}"

‎source/fundamentals/builders/aggregates.txt

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:atlas:`Limitations </atlas-vector-search/vector-search-stage/#limitations>`
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in the MongoDB Atlas documentation.
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Use the ``vectorSearch()`` method to create a :atlas:`$vectorSearch </atlas-vector-search/vector-search-stage/>`
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Use the ``vectorSearch()`` method to create a :atlas:`$vectorSearch
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</atlas-vector-search/vector-search-stage/>`
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pipeline stage that specifies a **semantic search**. A semantic search is
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a type of search which locates information that is similar in meaning.
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To use this feature, you must set up a vector search index and index your
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vector embeddings. To learn about how to programmatically create a
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vector search index, see the :ref:`java-search-indexes` section in the Indexes guide. To
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learn more about vector embeddings, see
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:atlas:`How to Index Vector Embeddings for Vector Search </atlas-search/field-types/knn-vector/>`.
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:atlas:`How to Index Vector Embeddings for Vector Search
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</atlas-search/field-types/knn-vector/>`.
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The following example shows how to build an aggregation pipeline that uses the
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``vectorSearch()`` and ``project()`` methods to compute a vector search score:
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:language: java
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:dedent:
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Learn more about this helper from the
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`vectorSearch() <{+api+}//apidocs/mongodb-driver-core/com/mongodb/client/model/Aggregates.html#vectorSearch(com.mongodb.client.model.search.FieldSearchPath,java.lang.Iterable,java.lang.String,long,long)>`__ API documentation.
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Learn more about this helper in the
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`vectorSearch()
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<{+api+}/apidocs/mongodb-driver-core/com/mongodb/client/model/Aggregates.html#vectorSearch(com.mongodb.client.model.search.FieldSearchPath,java.lang.Iterable,java.lang.String,long,com.mongodb.client.model.search.VectorSearchOptions)>`__
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API documentation.

‎source/fundamentals/indexes.txt

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:start-after: start create-search-index
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:end-before: end create-search-index
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To create multiple Search or Vector Search indexes, you must create a
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`SearchIndexModel
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<{+api+}/apidocs/mongodb-driver-core/com/mongodb/client/model/SearchIndexModel.html>`__
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instance for each index.
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The following code example shows how to create Search and
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Vector Search indexes in one call:
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‎source/includes/fundamentals/code-snippets/SearchIndexMethods.java

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import com.mongodb.client.*;
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import com.mongodb.client.model.SearchIndexModel;
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import com.mongodb.client.model.SearchIndexType;
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import org.bson.Document;
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import java.util.Arrays;

‎source/includes/fundamentals/code-snippets/builders/AggBuilders.java

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import static com.mongodb.client.model.Sorts.*;
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import static com.mongodb.client.model.Accumulators.*;
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import static com.mongodb.client.model.search.SearchPath.fieldPath;
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import static com.mongodb.client.model.search.VectorSearchOptions.vectorSearchOptions;
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import static com.mongodb.client.model.search.VectorSearchOptions.exactVectorSearchOptions;
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import static java.util.Arrays.asList;
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// end static import
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List<Double> queryVector = (asList(-0.0072121937, -0.030757688, -0.012945653));
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String indexName = "mflix_movies_embedding_index";
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FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
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int numCandidates = 2;
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int limit = 1;
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VectorSearchOptions options = vectorSearchOptions().filter(gte("year", 2016));
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VectorSearchOptions options = exactVectorSearchOptions().filter(gte("year", 2016));
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List<Bson> pipeline = asList(
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vectorSearch(
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fieldSearchPath,
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queryVector,
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indexName,
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numCandidates,
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limit,
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options),
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project(

‎source/includes/language-compatibility-table-java.rst

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.. [#virtual-threads-note] This driver version is not compatible with virtual threads.

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