DOCSP-45048 - Vector search params

source/fundamentals/builders.txt

@@ -439,14 +439,17 @@ have a defined Atlas Vector Search index before you can perform a vector search
    :atlas:`Create an Atlas Vector Search Index </atlas-vector-search/create-index>` in the
    Atlas manual.
 
+.. include:: /includes/vector-search-parameters.rst
+
 Consider the ``embedded_movies`` collection in the ``sample_mflix`` database. You
 can use a ``$vectorSearch`` stage to perform a semantic search on the ``plot_embedding``
 field of the documents in the collection.
 
 The following example shows how to use builders to generate an aggregation pipeline to
 perform the following operations:
 
-- Performs a vector search on the Atlas Vector Search index of the ``plot_embedding`` field using vector embeddings for the string ``"time travel"``
+- Performs a vector search on the Atlas Vector Search index of the ``plot_embedding``
+  field using vector embeddings for the string ``"time travel"``
 - Fetches the ``Title`` and ``Plot`` fields from documents found in the vector search
 
 .. code-block:: csharp
@@ -485,7 +488,8 @@ The results of the preceding example contain the following documents:
    { "_id" : ObjectId("573a13b6f29313caabd477fa"), "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050" }
    { "_id" : ObjectId("573a13e5f29313caabdc40c9"), "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal" }
 
-To learn more about Atlas Vector Search, see :atlas:`Atlas Vector Search Overview </atlas-vector-search/vector-search-overview/>`
+To learn more about Atlas Vector Search, see
+:atlas:`Atlas Vector Search Overview </atlas-vector-search/vector-search-overview/>`
 in the Atlas manual.
 
 Additional Information

source/fundamentals/linq.txt

@@ -640,6 +640,8 @@ defined Atlas Vector Search index before you can perform a vector search on your
    :atlas:`Create an Atlas Vector Search Index </atlas-vector-search/create-index>` in the
    Atlas manual.
 
+.. include:: /includes/vector-search-parameters.rst
+
 Consider the ``embedded_movies`` collection in the ``sample_mflix`` database. You
 can use a ``$vectorSearch`` stage to perform a semantic search on the ``plot_embedding``
 field of the documents in the collection.

source/includes/vector-search-parameters.rst

@@ -0,0 +1,68 @@
+To create a ``$vectorSearch`` pipeline stage, call the ``VectorSearch()`` method on a
+``PipelineStageDefinitionBuilder`` object. The ``VectorSearch()`` method accepts the
+following parameters:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 80
+
+   * - Parameter
+     - Description
+
+   * - ``field``
+     - The field to perform the vector search on.
+
+       **Data type**: ``Expression<Func<TInput, TField>>``
+
+   * - ``queryVector``
+     - The encoded vector that will be matched with values from the database.
+       Although the data type of this parameter is ``QueryVector``, you can also pass an
+       array of floating-point numbers.
+
+       **Data type**: `QueryVector <{+new-api-root+}/MongoDB.Driver/MongoDB.Driver.QueryVector.html>`__
+
+   * - ``limit``
+     - The maximum number of documents to return.
+
+       **Data type**: {+int-data-type+}
+
+   * - ``options``
+     - Configuration options for the vector search operation.
+
+       **Data type**: `VectorSearchOptions<TDocument> <{+new-api-root+}/MongoDB.Driver/MongoDB.Driver.VectorSearchOptions-1.html>`__
+
+You can use the ``options`` parameter to configure your vector search operation. The
+``VectorSearchOptions`` class contains the following properties:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 80
+
+   * - Property
+     - Description
+
+   * - ``Exact``
+     - Whether the vector search uses the exact nearest neighbor (ENN) algorithm.
+       If this property is set to ``false``, the vector search uses the approximate nearest
+       neighbor (ANN) algorithm.
+
+       **Data type**: {+bool-data-type+}
+       **Default**: ``false``
+
+   * - ``Filter``
+     - Additional search criteria that the found documents must match.
+
+       **Data Type:** `FilterDefinition<TDocument> <{+new-api-root+}/MongoDB.Driver/MongoDB.Driver.FilterDefinition-1.html>`__
+       **Default**: ``null``
+
+   * - ``IndexName``
+     - The index to perform the vector search on.
+
+       **Data type**: {+string-data-type+}
+       **Default**: ``null``
+
+   * - ``NumberOfCandidates``
+     - The number of neighbors to search in the index.
+
+       **Data type**: ``int?``
+       **Default**: ``null``