DOCSP-48164: Async examples for Indexes page

source/includes/indexes/indexes-async.py

@@ -0,0 +1,198 @@
+# start-index-single
+await movies.create_index("title")
+# end-index-single
+
+# start-index-single-collation
+from pymongo.collation import Collation
+
+await movies.create_index("title", collation=Collation(locale='fr_CA'))
+# end-index-single-collation
+
+# start-compound-index
+await movies.create_index([("type", pymongo.ASCENDING), ("genre", pymongo.ASCENDING)])
+# end-compound-index
+
+# start-compound-index-collation
+from pymongo.collation import Collation
+
+await movies.create_index([("type", pymongo.ASCENDING), ("genre", pymongo.ASCENDING)],
+                    collation=Collation(locale='fr_CA'))
+# end-compound-index-collation
+
+# start-index-multikey
+result = await movies.create_index("cast")
+# end-index-multikey
+
+# start-index-multikey-collation
+from pymongo.collation import Collation
+
+result = await movies.create_index("cast", collation=Collation(locale='fr_CA'))
+# end-index-multikey-collation
+
+# start-index-text-single
+await movies.create_index(
+    [( "plot", "text" )]
+)
+# end-index-text-single
+
+# start-index-text-single-collation
+from pymongo.collation import Collation
+
+await movies.create_index(
+    [( "plot", "text" )],
+    collation=Collation(locale='fr_CA')
+)
+# end-index-text-single-collation
+
+# start-index-text-multi
+from pymongo.collation import Collation
+
+result = await myColl.create_index(
+    [("title", "text"), ("genre", "text")],
+    default_language="english",
+    weights={ "title": 10, "genre": 3 },
+    collation=Collation(locale='fr_CA')
+)
+# end-index-text-multi
+
+# start-index-geo
+await theaters.create_index(
+    [( "location.geo", "2dsphere" )]
+)
+# end-index-geo
+
+# start-index-geo-collation
+from pymongo.collation import Collation
+
+await theaters.create_index(
+    [( "location.geo", "2dsphere" )],
+    collation=Collation(locale='fr_CA'))
+# end-index-geo-collation
+
+# start-index-wildcard
+await movies.create_index({ "location.$**": pymongo.ASCENDING })
+# end-index-wildcard
+
+# start-index-wildcard-collation
+await movies.create_index({ "location.$**": pymongo.ASCENDING },
+                    collation=Collation(locale='fr_CA'))
+# end-index-wildcard-collation
+
+# start-index-unique
+await theaters.create_index("theaterId", unique=True)
+# end-index-unique
+
+# start-index-unique-collation
+await theaters.create_index("theaterId", unique=True, collation=Collation(locale='fr_CA'))
+# end-index-unique-collation
+
+# start-index-clustered
+await sample_mflix.create_collection("movies", clusteredIndex={
+    "key": { "_id": 1 }, 
+    "unique": True
+})
+# end-index-clustered
+
+# start-remove-index
+await movies.drop_index("_title_")
+# end-remove-index
+
+# start-create-search-index
+index = {
+    "definition": {
+        "mappings": {
+            "dynamic": True
+        }
+    },
+    "name": "<index name>",
+}
+
+await collection.create_search_index(index)
+# end-create-search-index
+
+# start-create-vector-search-index
+from pymongo.operations import SearchIndexModel
+
+search_index_model = SearchIndexModel(
+  definition={
+    "fields": [
+      {
+        "type": "vector",
+        "numDimensions": <number of dimensions>,
+        "path": "<field to index>",
+        "similarity":  "<select from euclidean, cosine, dotProduct>"
+      }
+    ]
+  },
+  name="<index name>",
+  type="vectorSearch",
+)
+
+await collection.create_search_index(model=search_index_model)
+# end-create-vector-search-index
+
+# start-create-search-indexes
+search_idx = SearchIndexModel(
+    definition ={
+        "mappings": {
+            "dynamic": True
+        }
+    },
+    name="my_index",
+)
+
+vector_idx = SearchIndexModel(
+  definition={
+    "fields": [
+      {
+        "type": "vector",
+        "numDimensions": <number of dimensions>,
+        "path": "<field to index>",
+        "similarity": "<select from euclidean, cosine, dotProduct>"
+      }
+    ]
+  },
+  name="my_vector_index",
+  type="vectorSearch",
+)
+
+indexes = [search_idx, vector_idx]
+
+await collection.create_search_indexes(models=indexes)
+# end-create-search-indexes
+
+# start-list-search-indexes
+results = await collection.list_search_indexes().to_list()
+
+async for index in results:
+    print(index)
+# end-list-search-indexes
+
+# start-update-search-indexes
+new_index_definition = {
+    "mappings": {
+        "dynamic": False
+    }
+}
+
+await collection.update_search_index("my_index", new_index)
+# end-update-search-indexes
+
+# start-update-vector-search-indexes
+new_index_definition = {
+    "fields": [
+        {
+            "type": "vector",
+            "numDimensions": 1536,
+            "path": "<field to index>",
+            "similarity": "euclidean"
+        },
+    ]
+}
+
+await collection.update_search_index("my_vector_index", new_index_definition)
+# end-update-vector-search-indexes
+
+# start-delete-search-indexes
+await collection.drop_search_index("my_index")
+# end-delete-search-indexes