DOCSP-49759: Vector search index

source/includes/model-data/indexes.py

@@ -3,7 +3,7 @@
 from django.db.models import Q, F
 from django_mongodb_backend.models import EmbeddedModel
 from django_mongodb_backend.fields import EmbeddedModelField, ArrayField
-from django_mongodb_backend.indexes import SearchIndex
+from django_mongodb_backend.indexes import SearchIndex, VectorSearchIndex
 
 class Nutrition(EmbeddedModel):
     calories = models.IntegerField(default=0)
@@ -15,6 +15,7 @@ class Recipe(models.Model):
     cuisine = models.CharField(max_length=200)
     cook_time = models.IntegerField(default=0)
     allergens = ArrayField(models.CharField(max_length=100), null=True, blank=True)
+    ratings = ArrayField(models.IntegerField(default=0), size=10)
     nutrition = EmbeddedModelField(Nutrition, null=True, blank=True)
 
     class Meta:
@@ -72,6 +73,18 @@ class Meta:
     ]
 # end-atlas-search
 
+# start-vector-search
+class Meta:
+    db_table = "recipes"
+    indexes = [
+        VectorSearchIndex(
+            name=["vector_search_idx"],
+            fields=["ratings", "cook_time"],
+            similarities=["cosine", "euclidean"],
+        )
+    ]
+# end-vector-search
+
 # start-partial
 class Meta:
     db_table = "recipes"

source/model-data/indexes.txt

@@ -203,6 +203,7 @@ This section shows how to create the following advanced
 index types:
 
 - :ref:`django-indexes-atlas-search`
+- :ref:`django-indexes-vector-search`
 - :ref:`django-indexes-partial`
 - :ref:`django-indexes-unique`
 
@@ -240,6 +241,48 @@ an Atlas Search index named ``"title_search_idx"`` on the ``title`` field:
    - `SearchIndex <{+api+}ref/models/indexes/#searchindex>`__ class in the
      {+django-odm+} API documentation.
 
+.. _django-indexes-vector-search:
+
+Atlas Vector Search Indexes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Atlas Vector Search indexes allow you to query data based on its semantic meaning
+rather than by keyword matches. You can integrate vector search with full-text search
+queries and AI frameworks to support a range of use cases.
+
+To create a vector search index, assign the ``indexes`` option in your model's
+``Meta`` class to a ``VectorSearchIndex`` object. Pass the following arguments to the
+``VectorSearchIndex()`` constructor:
+
+- ``name``: *(Optional)* The name of your Atlas Vector Search index. If you do not
+  specify this argument, {+framework+} automatically generates an index name.
+- ``fields``: The fields you want to index. At least one must be a vector
+  field, represented by an array of ``FloatField`` or ``IntegerField`` values with
+  a `size <{+api+}ref/models/fields/#django_mongodb_backend.fields.ArrayField.size>`__.
+- ``similarities``: The :atlas:`similarity function </atlas-vector-search/vector-search-type/#std-label-avs-similarity-functions>`
+  to use. You can use the ``"cosine"``, ``"dotProduct"``, or ``"euclidean"``
+  similarity function. Specify the function as a single string value or a list
+  of values that assigns functions to individual vector fields.
+
+The following example updates the ``Recipe`` model's ``Meta`` class to create
+a vector search index named ``"vector_search_idx"`` on the ``ratings`` vector field
+and the ``cook_time`` numeric field:
+
+.. literalinclude:: /includes/model-data/indexes.py
+   :start-after: start-vector-search
+   :end-before: end-vector-search
+   :language: python
+   :copyable:
+   :emphasize-lines: 3-9
+
+.. tip::
+
+   To learn more about Atlas Vector Search queries and indexes, see the following resources:
+
+   - :atlas:`Atlas Vector Search </atlas-vector-search>` in the Atlas documentation.
+   - `VectorSearchIndex <{+api+}ref/models/indexes//#vectorsearchindex>`__ class in the
+     {+django-odm+} API documentation.
+
 .. _django-indexes-partial:
 
 Partial Indexes