enhance: add search_group_by example for milvus-client(#2481)

examples/search_group_by.py

@@ -0,0 +1,85 @@
+from pymilvus.milvus_client.milvus_client import MilvusClient
+from pymilvus import (
+    FieldSchema, CollectionSchema, DataType,
+)
+import numpy as np
+from typing import List
+
+collection_name = "test_milvus_client_iterator"
+prepare_new_data = False
+clean_exist = False
+
+USER_ID = "id"
+AGE = "age"
+DEPOSIT = "deposit"
+PICTURE = "picture"
+DIM = 8
+NUM_ENTITIES = 10000
+rng = np.random.default_rng(seed=19530)
+milvus_client = MilvusClient("http://localhost:19530")
+if milvus_client.has_collection(collection_name) and clean_exist:
+    milvus_client.drop_collection(collection_name)
+    print(f"dropped existed collection{collection_name}")
+
+if not milvus_client.has_collection(collection_name):
+    fields = [
+        FieldSchema(name=USER_ID, dtype=DataType.INT64, is_primary=True, auto_id=False),
+        FieldSchema(name=AGE, dtype=DataType.INT64),
+        FieldSchema(name=DEPOSIT, dtype=DataType.DOUBLE),
+        FieldSchema(name=PICTURE, dtype=DataType.FLOAT_VECTOR, dim=DIM)
+    ]
+    schema = CollectionSchema(fields)
+    milvus_client.create_collection(collection_name, dimension=DIM, schema=schema)
+
+if prepare_new_data:
+    entities = []
+    for i in range(NUM_ENTITIES):
+        entity = {
+            USER_ID: i,
+            AGE: (i % 100),
+            DEPOSIT: float(i),
+            PICTURE: rng.random((1, DIM))[0]
+        }
+        entities.append(entity)
+    milvus_client.insert(collection_name, entities)
+    milvus_client.flush(collection_name)
+    print(f"Finish flush collections:{collection_name}")
+
+index_params = milvus_client.prepare_index_params()
+
+index_params.add_index(
+    field_name=PICTURE,
+    index_type='IVF_FLAT',
+    metric_type='L2',
+    params={"nlist": 1024}
+)
+milvus_client.create_index(collection_name, index_params)
+milvus_client.load_collection(collection_name)
+
+nq = 3
+def print_res(result: List[List[dict]]):
+    for i in range(nq):
+        r = result[i]
+        print(f"search_group_by_res_i:{i}")
+        for e in r:
+            print(f"search_entity:{e}")
+        print(f"======================================================")
+
+
+vector_to_search = rng.random((nq, DIM), np.float32)
+# just search_group_by, no group_size, only 1 entities per group
+res = milvus_client.search(collection_name, data=vector_to_search, limit=10, anns_field=PICTURE,
+                     output_fields=[USER_ID, AGE], group_by_field=AGE)
+print_res(res)
+
+# search_group_by, with group_size=3, but not strict_group_size, entity number in per group may be 1~3
+res = milvus_client.search(collection_name, data=vector_to_search, limit=10, anns_field=PICTURE,
+                     output_fields=[USER_ID, AGE], group_by_field=AGE, group_size=3)
+print_res(res)
+
+# search_group_by, with group_size=3, and strict_group_size=true, entity number
+# in per group will be exactly 3 if data's sufficient
+res = milvus_client.search(collection_name, data=vector_to_search, limit=10, anns_field=PICTURE,
+                     output_fields=[USER_ID, AGE], group_by_field=AGE, group_size=3, strict_group_size=True)
+print_res(res)
+