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source/atlas-vector-search.txt

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@@ -63,13 +63,6 @@ The following example shows how to build an aggregation pipeline that uses the
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:language: java
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:dedent:
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.. tip:: Query Vector Type
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The preceding example creates an instance of ``BinaryVector`` to
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serve as the query vector, but you can also create a ``List`` of
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``Double`` instances. However, we recommend that you use the
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``BinaryVector`` type to improve storage efficiency.
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The following example shows how you can run the aggregation and print
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the vector search meta-score from the result of the preceding
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aggregation pipeline:
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- `Projections.metaVectorSearchScore()
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<{+api+}/apidocs/mongodb-driver-core/com/mongodb/client/model/Projections.html#metaVectorSearchScore(java.lang.String)>`__
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- `BinaryVector <{+api+}/apidocs/bson/org/bson/BinaryVector.html>`__

source/builders/aggregates.txt

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Learn more about this helper from the
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`searchMeta() API documentation <{+api+}/apidocs/mongodb-driver-core/com/mongodb/client/model/Aggregates.html#searchMeta(com.mongodb.client.model.search.SearchCollector)>`__.
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.. _java-atlas-vector-search:
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Atlas Vector Search
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-------------------
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.. important::
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To learn what versions of MongoDB Atlas support this feature, see
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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
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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
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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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.. literalinclude:: /includes/fundamentals/code-snippets/builders/AggBuilders.java
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:start-after: // begin vectorSearch
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:end-before: // end vectorSearch
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:language: java
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:dedent:
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The following example shows how you can print the score from the result of the
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preceding aggregation pipeline:
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.. literalinclude:: /includes/fundamentals/code-snippets/builders/AggBuilders.java
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:start-after: // begin vectorSearch-output
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:end-before: // end vectorSearch-output
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:language: java
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:dedent:
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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/builders/updates.txt

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.. _updates-builders:
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================
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Updates Builders
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================
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:depth: 2
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:class: singlecol
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.. _updates-builders:
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Overview
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--------
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source/crud/write-operations/modify.txt

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parameters:
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- ``query`` specifies a query filter with the criteria to match documents to update in your collection
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- ``updateDocument`` specifies the fields and values to modify in the matching document or documents. For this example, we use the :doc:`Updates builder </fundamentals/builders/updates>` to create the update document.
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- ``updateDocument`` specifies the fields and values to modify in the
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matching document or documents. For this example, we use the
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:ref:`Updates builder <updates-builders>` to create the
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update document.
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You can create the ``updateDocument`` using an ``Updates`` builder as
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follows:

source/databases-collections.txt renamed to source/getting-started/databases-collections.txt

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.. _java-databases-collections:
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=========================
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Databases and Collections
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=========================
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.. default-domain:: mongodb
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.. contents:: On this page
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:local:
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:backlinks: none

source/index.txt

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Getting Started </getting-started>
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Connect </connection>
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CRUD Operations </crud>
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Databases and Collations </databases-collections>
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Aggregation </aggregation>
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Builders </builders>
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Data Formats </data-formats>

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