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DOCSP-45096: binaryvector type for avs (#608)
* DOCSP-45096: binaryvector type for avs * wip * cross link * RM PR fixes 1 * add api links
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‎snooty.toml‎

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"/fundamentals/builders",
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"/fundamentals/aggregation",
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"/usage-examples",
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"/fundamentals/builders/aggregates",
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]
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sharedinclude_root = "https://raw.githubusercontent.com/10gen/docs-shared/main/"

‎source/fundamentals/builders.txt‎

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Builders
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========
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.. default-domain:: mongodb
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.. toctree::
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Aggregation </fundamentals/builders/aggregates>

‎source/fundamentals/builders/aggregates.txt‎

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:depth: 2
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:class: singlecol
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.. toctree::
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Atlas Vector Search </fundamentals/builders/vector-search>
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.. _aggregates-builders:
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Overview
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:language: java
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:dedent:
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Skip
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----
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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/fundamentals/builders/vector-search.txt‎

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.. _java-atlas-vector-search:
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===================
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Atlas Vector Search
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===================
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.. facet::
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:name: genre
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:values: reference
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.. meta::
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:keywords: code example, semantic, nearest
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.. contents:: On this page
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:local:
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:backlinks: none
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:depth: 2
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:class: singlecol
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Overview
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--------
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In this guide, you can learn how to use the :atlas:`Atlas Vector Search
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</atlas-vector-search/vector-search-overview/>` feature
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in the {+driver-short+}. The ``Aggregates`` builders class provides the
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the ``vectorSearch()`` helper method that you can use to
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create a :atlas:`$vectorSearch </atlas-vector-search/vector-search-stage/>`
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pipeline stage. This pipeline stage allows you to perform a **semantic
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search** on your documents. A semantic search is a type of search which
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locates information that is similar in meaning, but not necessarily
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identical, to your provided search term or phrase.
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.. important:: Feature Compatibility
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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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Perform a Vector Search
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-----------------------
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To use this feature, you must create 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
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Indexes guide. To 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/>` in the Atlas documentation.
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After you create a vector search index on your vector embeddings, you
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can reference this index in your pipeline stage, as shown in the
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following section.
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Vector Search Example
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~~~~~~~~~~~~~~~~~~~~~
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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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.. 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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.. 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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.. tip:: Java Driver Vector Search Examples
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Visit the :atlas:`Atlas documentation </atlas-vector-search/tutorials/>`
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to find more tutorials on using the {+driver-short+} to perform Atlas
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Vector Searches.
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API Documentation
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-----------------
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To learn more about the methods and types mentioned in this
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guide, see the following API documentation:
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- `Aggregates.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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- `FieldSearchPath
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<{+api+}/apidocs/mongodb-driver-core/com/mongodb/client/model/search/FieldSearchPath.html>`__
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- `VectorSearchOptions
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<{+api+}/apidocs/mongodb-driver-core/com/mongodb/client/model/search/VectorSearchOptions.html>`__
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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/fundamentals/indexes.txt‎

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Atlas Vector Search enables you to perform semantic searches on vector
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embeddings stored in MongoDB Atlas. To learn more about Atlas Vector Search, see the
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:ref:`java-atlas-vector-search` section in the Aggregates Builder guide.
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:ref:`java-atlas-vector-search` guide.
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You can call the following methods on a collection to manage your Atlas
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Search and Vector Search indexes:

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

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private void vectorSearchPipeline() {
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// begin vectorSearch
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List<Double> queryVector = (asList(-0.0072121937, -0.030757688, -0.012945653));
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// Create an instance of the BinaryVector class as the query vector
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BinaryVector queryVector = BinaryVector.floatVector(
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new float[]{0.0001f, 1.12345f, 2.23456f, 3.34567f, 4.45678f});
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// Specify the index name for the vector embedding index
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String indexName = "mflix_movies_embedding_index";
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// Specify the path of the field to search on
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FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
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// Limit the number of matches to 1
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int limit = 1;
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VectorSearchOptions options = exactVectorSearchOptions().filter(gte("year", 2016));
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// Create a pre-filter to only search within a subset of documents
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VectorSearchOptions options = exactVectorSearchOptions()
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.filter(gte("year", 2016));
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// Create the vectorSearch pipeline stage
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List<Bson> pipeline = asList(
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vectorSearch(
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fieldSearchPath,

‎source/whats-new.txt‎

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.. replacement:: vector-type-example-link
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To learn about how to use this type when using the Atlas
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Vector Search feature, see the TODO add link :ref:`` guide.
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Vector Search feature, see the :ref:`java-atlas-vector-search` guide.
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- Adds a client bulk write API that allows you to perform write operations on multiple
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databases and collections at once. To learn more about this feature, see the

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