Add test script

example/index_support/prepare_data.py

@@ -0,0 +1,122 @@
+# hello_milvus.py demonstrates the basic operations of PyMilvus, a Python SDK of Milvus.
+# 1. connect to Milvus
+# 2. create collection
+# 3. insert data
+# 4. create index
+# 5. search, query, and hybrid search on entities
+# 6. delete entities by PK
+# 7. drop collection
+import time
+import os
+import numpy as np
+from pymilvus import (
+    connections,
+    utility,
+    FieldSchema, CollectionSchema, DataType,
+    Collection,
+)
+
+fmt = "\n=== {:30} ===\n"
+search_latency_fmt = "search latency = {:.4f}s"
+num_entities, dim = 3000, 8
+
+#################################################################################
+# 1. connect to Milvus
+# Add a new connection alias `default` for Milvus server in `localhost:19530`
+# Actually the "default" alias is a buildin in PyMilvus.
+# If the address of Milvus is the same as `localhost:19530`, you can omit all
+# parameters and call the method as: `connections.connect()`.
+#
+# Note: the `using` parameter of the following methods is default to "default".
+print(fmt.format("start connecting to Milvus"))
+
+host = os.environ.get('MILVUS_HOST')
+if host == None:
+    host = "localhost"
+print(fmt.format(f"Milvus host: {host}"))
+connections.connect("default", host=host, port="19530")
+
+has = utility.has_collection("hello_milvus")
+print(f"Does collection hello_milvus exist in Milvus: {has}")
+
+#################################################################################
+# 2. create collection
+# We're going to create a collection with 3 fields.
+# +-+------------+------------+------------------+------------------------------+
+# | | field name | field type | other attributes |       field description      |
+# +-+------------+------------+------------------+------------------------------+
+# |1|    "pk"    |    Int64   |  is_primary=True |      "primary field"         |
+# | |            |            |   auto_id=False  |                              |
+# +-+------------+------------+------------------+------------------------------+
+# |2|  "random"  |    Double  |                  |      "a double field"        |
+# +-+------------+------------+------------------+------------------------------+
+# |3|"embeddings"| FloatVector|     dim=8        |  "float vector with dim 8"   |
+# +-+------------+------------+------------------+------------------------------+
+fields = [
+    FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=False),
+    FieldSchema(name="random", dtype=DataType.DOUBLE),
+    FieldSchema(name="var", dtype=DataType.VARCHAR, max_length=65535),
+    FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim)
+]
+
+schema = CollectionSchema(fields, "hello_milvus")
+
+print(fmt.format("Create collection `hello_milvus`"))
+hello_milvus = Collection("hello_milvus", schema, consistency_level="Strong")
+
+################################################################################
+# 3. insert data
+# We are going to insert 3000 rows of data into `hello_milvus`
+# Data to be inserted must be organized in fields.
+#
+# The insert() method returns:
+# - either automatically generated primary keys by Milvus if auto_id=True in the schema;
+# - or the existing primary key field from the entities if auto_id=False in the schema.
+
+print(fmt.format("Start inserting entities"))
+rng = np.random.default_rng(seed=19530)
+entities = [
+    # provide the pk field because `auto_id` is set to False
+    [i for i in range(num_entities)],
+    rng.random(num_entities).tolist(),  # field random, only supports list
+    [str(i) for i in range(num_entities)],
+    rng.random((num_entities, dim)),    # field embeddings, supports numpy.ndarray and list
+]
+
+insert_result = hello_milvus.insert(entities)
+hello_milvus.flush()
+print(f"Number of entities in hello_milvus: {hello_milvus.num_entities}")  # check the num_entites
+
+# create another collection
+fields2 = [
+    FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True),
+    FieldSchema(name="random", dtype=DataType.DOUBLE),
+    FieldSchema(name="var", dtype=DataType.VARCHAR, max_length=65535),
+    FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim)
+]
+
+schema2 = CollectionSchema(fields2, "hello_milvus2")
+
+print(fmt.format("Create collection `hello_milvus2`"))
+hello_milvus2 = Collection("hello_milvus2", schema2, consistency_level="Strong")
+
+entities2 = [
+    rng.random(num_entities).tolist(),  # field random, only supports list
+    [str(i) for i in range(num_entities)],
+    rng.random((num_entities, dim)),    # field embeddings, supports numpy.ndarray and list
+]
+
+insert_result2 = hello_milvus2.insert(entities2)
+hello_milvus2.flush()
+insert_result2 = hello_milvus2.insert(entities2)
+hello_milvus2.flush()
+
+index_params = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
+hello_milvus.create_index("embeddings", index_params)
+
+hello_milvus2.create_index("embeddings", index_params)
+index_params2 = {"index_type": "Trie"}
+hello_milvus2.create_index("var", index_params2)
+
+print(f"Number of entities in hello_milvus2: {hello_milvus2.num_entities}")  # check the num_entites
+