Refine interfaces for milvus_client

The main focus of this PR is to port some interfaces from ORM
and transform them from an object-oriented form to a functional form.
As these are new interfaces of MilvusClient, certain parameters and return values
have been modified compared to the original ORM interfaces.

Signed-off-by: zhenshan.cao <[email protected]>

examples/milvus_client/alias.py

@@ -0,0 +1,67 @@
+import time
+import numpy as np
+from pymilvus import (
+    MilvusClient,
+)
+
+fmt = "\n=== {:30} ===\n"
+dim = 8
+collection_name = "hello_milvus"
+milvus_client = MilvusClient("http://localhost:19530")
+milvus_client.drop_collection(collection_name)
+milvus_client.create_collection(collection_name, dim, consistency_level="Strong", metric_type="L2", auto_id=True)
+
+collection_name2 = "hello_milvus2"
+milvus_client.drop_collection(collection_name2)
+milvus_client.create_collection(collection_name2, dim, consistency_level="Strong", metric_type="L2", auto_id=True)
+
+
+print("collections:", milvus_client.list_collections())
+
+desc_c1 = milvus_client.describe_collection(collection_name)
+print(f"{collection_name} :", desc_c1)
+
+rng = np.random.default_rng(seed=19530)
+
+rows = [
+    {"vector": rng.random((1, dim))[0], "a": 100},
+    {"vector": rng.random((1, dim))[0], "b": 200},
+    {"vector": rng.random((1, dim))[0], "c": 300},
+]
+
+print(fmt.format(f"Start inserting entities to {collection_name}"))
+insert_result = milvus_client.insert(collection_name, rows)
+print(insert_result)
+
+rows = [
+    {"vector": rng.random((1, dim))[0], "d": 400},
+    {"vector": rng.random((1, dim))[0], "e": 500},
+    {"vector": rng.random((1, dim))[0], "f": 600},
+]
+
+print(fmt.format(f"Start inserting entities to {collection_name2}"))
+insert_result2 = milvus_client.insert(collection_name2, rows)
+print(insert_result2)
+
+alias = "alias_hello_milvus"
+milvus_client.create_alias(collection_name, alias)
+
+assert milvus_client.describe_collection(alias) == milvus_client.describe_collection(collection_name)
+
+milvus_client.alter_alias(collection_name2, alias)
+assert milvus_client.describe_collection(alias) == milvus_client.describe_collection(collection_name2)
+
+query_results = milvus_client.query(alias, filter= "f == 600")
+print("results of query 'f == 600' is ")
+for ret in query_results: 
+    print(ret)
+
+
+milvus_client.drop_alias(alias)
+has_collection = milvus_client.has_collection(alias)
+assert not has_collection
+has_collection = milvus_client.has_collection(collection_name2)
+assert has_collection
+
+milvus_client.drop_collection(collection_name)
+milvus_client.drop_collection(collection_name2)

examples/milvus_client/customize_schema.py

@@ -0,0 +1,70 @@
+import time
+import numpy as np
+from pymilvus import (
+    MilvusClient,
+    DataType
+)
+
+fmt = "\n=== {:30} ===\n"
+dim = 8
+collection_name = "hello_milvus"
+milvus_client = MilvusClient("http://localhost:19530")
+
+has_collection = milvus_client.has_collection(collection_name, timeout=5)
+if has_collection:
+    milvus_client.drop_collection(collection_name)
+
+schema = milvus_client.create_schema(enable_dynamic_field=True)
+schema.add_field("id", DataType.INT64, is_primary=True)
+schema.add_field("embeddings", DataType.FLOAT_VECTOR, dim=dim)
+schema.add_field("title", DataType.VARCHAR, max_length=64)
+
+
+index_params = milvus_client.prepare_index_params()
+index_params.add_index(field_name = "embeddings", metric_type="L2")
+milvus_client.create_collection(collection_name, schema=schema, index_params=index_params, consistency_level="Strong")
+
+print(fmt.format("    all collections    "))
+print(milvus_client.list_collections())
+
+print(fmt.format(f"schema of collection {collection_name}"))
+print(milvus_client.describe_collection(collection_name))
+
+rng = np.random.default_rng(seed=19530)
+rows = [
+        {"id": 1, "embeddings": rng.random((1, dim))[0], "a": 100, "title": "t1"},
+        {"id": 2, "embeddings": rng.random((1, dim))[0], "b": 200, "title": "t2"},
+        {"id": 3, "embeddings": rng.random((1, dim))[0], "c": 300, "title": "t3"},
+        {"id": 4, "embeddings": rng.random((1, dim))[0], "d": 400, "title": "t4"},
+        {"id": 5, "embeddings": rng.random((1, dim))[0], "e": 500, "title": "t5"},
+        {"id": 6, "embeddings": rng.random((1, dim))[0], "f": 600, "title": "t6"},
+]
+
+print(fmt.format("Start inserting entities"))
+insert_result = milvus_client.insert(collection_name, rows)
+print(fmt.format("Inserting entities done"))
+print(insert_result)
+
+
+print(fmt.format("Start load collection "))
+milvus_client.load_collection(collection_name)
+
+print(fmt.format("Start query by specifying primary keys"))
+query_results = milvus_client.query(collection_name, ids=[2])
+print(query_results[0])
+
+print(fmt.format("Start query by specifying filtering expression"))
+query_results = milvus_client.query(collection_name, filter= "f == 600 or title == 't2'")
+for ret in query_results: 
+    print(ret)
+
+rng = np.random.default_rng(seed=19530)
+vectors_to_search = rng.random((1, dim))
+
+print(fmt.format(f"Start search with retrieve serveral fields."))
+result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["pk", "a", "b"])
+for hits in result:
+    for hit in hits:
+        print(f"hit: {hit}")
+
+milvus_client.drop_collection(collection_name)

examples/milvus_client/index.py

@@ -0,0 +1,87 @@
+import time
+import numpy as np
+from pymilvus import (
+    MilvusClient,
+    DataType
+)
+
+fmt = "\n=== {:30} ===\n"
+dim = 8
+collection_name = "hello_milvus"
+milvus_client = MilvusClient("http://localhost:19530")
+
+has_collection = milvus_client.has_collection(collection_name, timeout=5)
+if has_collection:
+    milvus_client.drop_collection(collection_name)
+
+schema = milvus_client.create_schema(enable_dynamic_field=True)
+schema.add_field("id", DataType.INT64, is_primary=True)
+schema.add_field("embeddings", DataType.FLOAT_VECTOR, dim=dim)
+schema.add_field("title", DataType.VARCHAR, max_length=64)
+
+# collection is not loaded after creation
+milvus_client.create_collection(collection_name, schema=schema, consistency_level="Strong")
+
+rng = np.random.default_rng(seed=19530)
+rows = [
+        {"id": 1, "embeddings": rng.random((1, dim))[0], "a": 100, "title": "t1"},
+        {"id": 2, "embeddings": rng.random((1, dim))[0], "b": 200, "title": "t2"},
+        {"id": 3, "embeddings": rng.random((1, dim))[0], "c": 300, "title": "t3"},
+        {"id": 4, "embeddings": rng.random((1, dim))[0], "d": 400, "title": "t4"},
+        {"id": 5, "embeddings": rng.random((1, dim))[0], "e": 500, "title": "t5"},
+        {"id": 6, "embeddings": rng.random((1, dim))[0], "f": 600, "title": "t6"},
+]
+
+print(fmt.format("Start inserting entities"))
+insert_result = milvus_client.insert(collection_name, rows)
+print(fmt.format("Inserting entities done"))
+print(insert_result)
+
+index_params = milvus_client.prepare_index_params()
+index_params.add_index(field_name = "embeddings", metric_type="L2")
+index_params.add_index(field_name = "title", index_type = "TRIE", index_name="my_trie")
+
+print(fmt.format("Start create index"))
+milvus_client.create_index(collection_name, index_params)
+
+
+index_names = milvus_client.list_indexes(collection_name)
+print(f"index names for {collection_name}:", index_names)
+for index_name in index_names:
+    index_info = milvus_client.describe_index(collection_name, index_name=index_name)
+    print(f"index info for index {index_name} is:", index_info)
+
+print(fmt.format("Start load collection"))
+milvus_client.load_collection(collection_name)
+
+print(fmt.format("Start query by specifying primary keys"))
+query_results = milvus_client.query(collection_name, ids=[2])
+print(query_results[0])
+
+print(fmt.format("Start query by specifying filtering expression"))
+query_results = milvus_client.query(collection_name, filter= "f == 600 or title == 't2'")
+for ret in query_results: 
+    print(ret)
+
+vectors_to_search = rng.random((1, dim))
+print(fmt.format(f"Start search with retrieve serveral fields."))
+result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["title"])
+for hits in result:
+    for hit in hits:
+        print(f"hit: {hit}")
+
+
+
+field_index_names = milvus_client.list_indexes(collection_name, field_name = "embeddings")
+print(f"index names for {collection_name}`s field embeddings:", field_index_names)
+
+try:
+    milvus_client.drop_index(collection_name, "my_trie")
+except Exception as e:
+    print(f"cacthed {e}")
+
+milvus_client.release_collection(collection_name)
+
+milvus_client.drop_index(collection_name, "my_trie")
+
+milvus_client.drop_collection(collection_name)