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499 lines (432 loc) · 52.9 KB
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import unittest
import uuid as uuid_lib
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from unittest.mock import Mock, patch
import pytest
from test.util import check_error_message
from weaviate.exceptions import SchemaValidationException
from weaviate.util import (
MINIMUM_NO_WARNING_VERSION,
_datetime_from_weaviate_str,
_is_sub_schema,
_sanitize_str,
generate_uuid5,
get_domain_from_weaviate_url,
get_valid_uuid,
get_vector,
image_decoder_b64,
image_encoder_b64,
is_object_url,
is_weaviate_client_too_old,
is_weaviate_object_url,
is_weaviate_too_old,
parse_version_string,
)
schema_set = {
"classes": [
{"class": "Ollie", "properties": [{"name": "height"}]},
{"class": "Shuvit", "properties": [{"name": "direction"}]},
{"class": "Board", "properties": [{"name": "brand"}, {"name": "art"}, {"name": "size"}]},
{"class": "Truck", "properties": [{"name": "name"}, {"name": "height"}]},
],
}
schema_set_extended_prop = {
"classes": [
{"class": "Ollie", "properties": [{"name": "weight"}]},
{"class": "Shuvit", "properties": [{"name": "direction"}]},
{"class": "Board", "properties": [{"name": "brand"}, {"name": "art"}, {"name": "size"}]},
{"class": "Truck", "properties": [{"name": "name"}, {"name": "height"}]},
],
}
schema_sub_set = {
"classes": [
{"class": "Ollie", "properties": [{"name": "height"}]},
{"class": "Board", "properties": [{"name": "brand"}, {"name": "art"}, {"name": "size"}]},
],
}
disjoint_set = {
"classes": [
{"class": "Manual", "properties": [{"name": "nose"}]},
{"class": "Bearings", "properties": [{"name": "brand"}]},
],
}
partial_set = {
"classes": [
{"class": "Board", "properties": [{"name": "brand"}, {"name": "art"}, {"name": "size"}]},
{"class": "Truck", "properties": [{"name": "name"}, {"name": "height"}]},
{"class": "Bearings", "properties": [{"name": "brand"}]},
{"class": "Ollie", "properties": [{"name": "height"}]},
{"class": "Shuvit", "properties": [{"name": "direction"}]},
{"class": "Manual", "properties": [{"name": "nose"}]},
],
}
schema_company = {
"classes": [
{
"class": "Company",
"description": "A business that acts in the market",
"properties": [
{
"name": "name",
"description": "The name under which the company is known",
"dataType": ["text"],
},
{
"name": "legalBody",
"description": "The legal body under which the company maintains its business",
"dataType": ["text"],
},
{
"name": "hasEmployee",
"description": "The employees of the company",
"dataType": ["Employee"],
},
],
},
{
"class": "Employee",
"description": "An employee of the company",
"properties": [
{"name": "name", "description": "The name of the employee", "dataType": ["text"]},
{
"name": "job",
"description": "the job description of the employee",
"dataType": ["text"],
},
{
"name": "yearsInTheCompany",
"description": "The number of years this employee has worked in the company",
"dataType": ["int"],
},
],
},
]
}
class TestUtil(unittest.TestCase):
def test_is_weaviate_object_url(self):
"""Test the `is_weaviate_object_url` function."""
# valid formats
self.assertTrue(
is_weaviate_object_url("weaviate://localhost/28f3f61b-b524-45e0-9bbe-2c1550bf73d2")
)
self.assertTrue(
is_weaviate_object_url(
"weaviate://some-domain.com/28f3f61b-b524-45e0-9bbe-2c1550bf73d2"
)
)
# invalid formats
## wrong argument data type
self.assertFalse(
is_weaviate_object_url(["weaviate://localhost/28f3f61b-b524-45e0-9bbe-2c1550bf73d2"])
)
## wrong prefix, i.e. does not start with 'weaviate://'
self.assertFalse(
is_weaviate_object_url("http://some-domain.com/28f3f61b-b524-45e0-9bbe-2c1550bf73d2")
)
## wrong path, additional '/thing'
self.assertFalse(
is_weaviate_object_url("weaviate://localhost/things/f61b-b524-45e0-9bbe-2c1550bf73d2")
)
## wrong domain format
self.assertFalse(
is_weaviate_object_url(
"weaviate://some-INVALID-domain/28f3f61b-b524-45e0-9bbe-2c1550bf73d2"
)
)
# wrong UUID format
self.assertFalse(is_weaviate_object_url("weaviate://localhost/UUID"))
def test_is_object_url(self):
"""Test the `is_object_url` function."""
# valid formats
self.assertTrue(
is_object_url("http://localhost:8080/v1/objects/1c9cd584-88fe-5010-83d0-017cb3fcb446")
)
self.assertTrue(
is_object_url("http://ramalamadingdong/v1/objects/1c9cd584-88fe-5010-83d0-017cb3fcb446")
)
# invalid formats
## wrong path, should be at least 3 subpaths to the object UUID
self.assertFalse(is_object_url("objects/1c9cd584-88fe-5010-83d0-017cb3fcb446"))
## wrong '/v2', shoudl be '/v1'
self.assertFalse(
is_object_url("http://localhost:8080/v2/objects/1c9cd584-88fe-5010-83d0-017cb3fcb446")
)
## wrong UUID format
self.assertFalse(
is_object_url("http://ramalamadingdong/v1/objects/1c9cd584-88fe-5010-83d0")
)
## wrong objects path, instead of '/passions' should have been '/objects/
self.assertFalse(
is_object_url("http://localhost:8080/v1/passions/1c9cd584-88fe-5010-83d0-017cb3fcb446")
)
def test_get_valid_uuid(self):
"""Test the `get_valid_uuid` function."""
# valid calls
result = get_valid_uuid("weaviate://localhost/28f3f61b-b524-45e0-9bbe-2c1550bf73d2")
self.assertEqual(result, "28f3f61b-b524-45e0-9bbe-2c1550bf73d2")
result = get_valid_uuid("weaviate://otherhost.com/28f3f61b-b524-45e0-9bbe-2c1550bf73d2")
self.assertEqual(result, "28f3f61b-b524-45e0-9bbe-2c1550bf73d2")
result = get_valid_uuid(
"http://localhost:8080/v1/objects/1c9cd584-88fe-5010-83d0-017cb3fcb446"
)
self.assertEqual(result, "1c9cd584-88fe-5010-83d0-017cb3fcb446")
result = get_valid_uuid(
"http://otherhost_2:8080/v1/objects/1c9cd584-88fe-5010-83d0-017cb3fcb446"
)
self.assertEqual(result, "1c9cd584-88fe-5010-83d0-017cb3fcb446")
result = get_valid_uuid(
"http://otherhost_2:8080/v1/objects/1c9cd58488fe501083d0017cb3fcb446"
)
self.assertEqual(result, "1c9cd584-88fe-5010-83d0-017cb3fcb446")
result = get_valid_uuid("1c9cd584-88fe-5010-83d0-017cb3fcb446")
self.assertEqual(result, "1c9cd584-88fe-5010-83d0-017cb3fcb446")
result = get_valid_uuid("1c9cd58488fe501083d0017cb3fcb446")
self.assertEqual(result, "1c9cd584-88fe-5010-83d0-017cb3fcb446")
result = get_valid_uuid(uuid_lib.UUID("1c9cd58488fe501083d0017cb3fcb446"))
self.assertEqual(result, "1c9cd584-88fe-5010-83d0-017cb3fcb446")
# invalid formats
type_error_message = "'uuid' must be of type str or uuid.UUID, but was: "
value_error_message = "Not valid 'uuid' or 'uuid' can not be extracted from value"
## neither an object URL nor a weaviate object URL
with self.assertRaises(ValueError) as error:
get_valid_uuid("http://localhost:8080/v1/1c9cd584-88fe-5010-83d0-017cb3fcb")
check_error_message(self, error, value_error_message)
# wrong UUID format
with self.assertRaises(ValueError) as error:
get_valid_uuid("http://localhost:8080/v1/objects/some-UUID")
check_error_message(self, error, value_error_message)
## wrong '/v2', shoudl be '/v1'
with self.assertRaises(ValueError) as error:
get_valid_uuid("http://localhost:8080/v2/objects/1c9cd584-88fe-5010-83d0-017cb3fcb")
check_error_message(self, error, value_error_message)
## wrong URL
with self.assertRaises(ValueError) as error:
get_valid_uuid("weaviate://INVALID_URL//1c9cd584-88fe-5010-83d0-017cb3fcb")
check_error_message(self, error, value_error_message)
## wrong UUID data type
with self.assertRaises(TypeError) as error:
get_valid_uuid(12)
check_error_message(self, error, type_error_message + str(int))
def test_get_vector(self):
"""Test the `get_vector` function."""
vector_list = [1.0, 2.0, 3.0]
# vector is a list
self.assertEqual(get_vector(vector_list), vector_list)
# vector is a `torch.Tensor` or `numpy.ndarray`
vector_mock = Mock()
mock_tolist = Mock()
mock_tolist.tolist.return_value = vector_list
mock_squeeze = Mock(return_value=mock_tolist)
vector_mock.squeeze = mock_squeeze
self.assertEqual(get_vector(vector_mock), vector_list)
# vector is a `tf.Tensor`
vector_mock = Mock()
mock_tolist = Mock()
mock_squeeze = Mock()
mock_tolist.tolist.return_value = vector_list
mock_squeeze.squeeze.return_value = mock_tolist
mock_numpy = Mock(return_value=mock_squeeze)
vector_mock.numpy = mock_numpy
vector_mock.squeeze = Mock(side_effect=AttributeError("TEST TensorFlow Tensor"))
self.assertEqual(get_vector(vector_mock), vector_list)
# invalid call
type_error_message = (
"The type of the 'vector' argument is not supported!\n"
"Supported types are `list`, 'numpy.ndarray`, `torch.Tensor`, `tf.Tensor`, `pd.Series`, and `pl.Series`"
)
with self.assertRaises(TypeError) as error:
get_vector("[1., 2., 3.]")
check_error_message(self, error, type_error_message)
def test_get_domain_from_weaviate_url(self):
"""Test the `get_domain_from_weaviate_url` function."""
uuid = "28f3f61b-b524-45e0-9bbe-2c1550bf73d2"
self.assertEqual(get_domain_from_weaviate_url(f"weaviate://localhost/{uuid}"), "localhost")
self.assertEqual(get_domain_from_weaviate_url(f"weaviate://otherhost/{uuid}"), "otherhost")
def test__is_sub_schema(self):
"""Test the `_is_sub_schema` function."""
self.assertTrue(_is_sub_schema(schema_set, schema_set))
self.assertTrue(_is_sub_schema(schema_set["classes"][0], schema_set))
self.assertTrue(_is_sub_schema(schema_sub_set, schema_set))
schema_set_copy = deepcopy(schema_set)
for schema_class in schema_set_copy["classes"]:
schema_class["class"] = schema_class["class"].lower()
self.assertTrue(_is_sub_schema(schema_set_copy, schema_set))
self.assertTrue(_is_sub_schema(schema_set, schema_set_copy))
self.assertTrue(_is_sub_schema(schema_set_copy, schema_set_copy))
self.assertFalse(_is_sub_schema({"class": "A"}, schema_set))
self.assertFalse(_is_sub_schema(disjoint_set, schema_set))
self.assertFalse(_is_sub_schema(partial_set, schema_set))
self.assertFalse(_is_sub_schema(schema_set_extended_prop, schema_set))
# invalid calls
invalid_sub_schema_msg = "The sub schema class/es MUST have a 'class' keyword each!"
with self.assertRaises(SchemaValidationException) as error:
_is_sub_schema({}, schema_set)
check_error_message(self, error, invalid_sub_schema_msg)
def test_image_encoder_b64(self):
"""Test the `image_encoder_b64` function."""
encode_weaviate_logo_b64 = "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"
file_path_value_error_message = "No file found at location non-existingfile.png"
type_error_message = (
'"image_or_image_path" should be a image path or a binary read file (io.BufferedReader)'
)
# invalid calls
with self.assertRaises(ValueError) as error:
image_encoder_b64("non-existingfile.png")
check_error_message(self, error, file_path_value_error_message)
with self.assertRaises(TypeError) as error:
image_encoder_b64(True)
check_error_message(self, error, type_error_message)
with self.assertRaises(TypeError) as error:
with open("image.png", "wb") as file:
image_encoder_b64(file)
check_error_message(self, error, type_error_message)
# valid calls
encrypted_1 = image_encoder_b64("integration/weaviate-logo.png")
self.assertEqual(encrypted_1, encode_weaviate_logo_b64)
self.assertIsInstance(encrypted_1, str)
with open("integration/weaviate-logo.png", "rb") as file:
encrypted_2 = image_encoder_b64(file)
self.assertEqual(encrypted_2, encode_weaviate_logo_b64)
self.assertIsInstance(encrypted_2, str)
def test_image_decoder_b64(self):
"""Test the `image_decoder_b64` function."""
encode_weaviate_logo_b64 = "iVBORw0KGgoAAAANSUhEUgAAAZAAAAE5CAYAAAC+rHbqAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAyhpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw/eHBhY2tldCBiZWdpbj0i77u/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8+IDx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IkFkb2JlIFhNUCBDb3JlIDUuNi1jMTQ1IDc5LjE2MzQ5OSwgMjAxOC8wOC8xMy0xNjo0MDoyMiAgICAgICAgIj4gPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4gPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIgeG1sbnM6eG1wPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvIiB4bWxuczp4bXBNTT0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL21tLyIgeG1sbnM6c3RSZWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZVJlZiMiIHhtcDpDcmVhdG9yVG9vbD0iQWRvYmUgUGhvdG9zaG9wIENDIDIwMTkgKE1hY2ludG9zaCkiIHhtcE1NOkluc3RhbmNlSUQ9InhtcC5paWQ6Rjc3MkEzQzdGM0QxMTFFODlBRTRBNjMyNUE2MTk3NzgiIHhtcE1NOkRvY3VtZW50SUQ9InhtcC5kaWQ6Rjc3MkEzQzhGM0QxMTFFODlBRTRBNjMyNUE2MTk3NzgiPiA8eG1wTU06RGVyaXZlZEZyb20gc3RSZWY6aW5zdGFuY2VJRD0ieG1wLmlpZDpGNzcyQTNDNUYzRDExMUU4OUFFNEE2MzI1QTYxOTc3OCIgc3RSZWY6ZG9jdW1lbnRJRD0ieG1wLmRpZDpGNzcyQTNDNkYzRDExMUU4OUFFNEE2MzI1QTYxOTc3OCIvPiA8L3JkZjpEZXNjcmlwdGlvbj4gPC9yZGY6UkRGPiA8L3g6eG1wbWV0YT4gPD94cGFja2V0IGVuZD0iciI/PnOiDKoAABWzSURBVHja7N3feRNJugfgWj9zfzgRrJwBngjk+7mACEZOYMARYEdgmASsjQAu5h6dBAYysE4GnAg4/Vkl0BjbyJJaqqp+3+fRss/sLJjqVv3qX3/9r69fvyYAeKojTQDAJn7RBAD3+/W3P551v1x1n8u///pzrkXMQH52w7zoPs+1BNB5030m3eem6xcucqCQ/cseyLfgGHW/XHefcff53I02TrQKDL5PuLnzj790n/Ouf5hqIQGynKLGKOP1nf8pbpK3bhEYbN/wMQ8o7zNLi2WtmQAZ7g3yOofHfdPSGGkcdzfIF18lGFzfEMHxcY1/dZoGvD9yNNSbo/vE1PTqgfBI+Z+/8VWCQbpe89+bdJ9PsT9iBtJ+cIxyaLx4wv/t2OkLGNQA82LDwWP0E7H0/UGAtHVDxGzi9YY3xay7IU59rWAQ4RF9RaxObHPaapaD5HPr7XU0gBtikm+ITZejxnk9FGjf1ZbhcdtnpMWy1nXrx36bnYHkTj9uhl080zHvRhPHvlvQ9GAz+opPO/5t4xDOZasnOpsLkLzPsXz4Z5fiJrjwNYNmA+SxY7tbD0K7z1lrx36bCZCVfY5XO5iCPjSScKwX2gyPGHBe7+GPmuUgmbfQbkeNXPwXeer5pqfwSPn3vfJVg+bCY59H9mOGE2VRrlrYHzmq/MI/z9PO991ntIc/cqJOFjTn9Z76j7t/5k2e+VSryiWslQqZh2h8x3qhndlHBMen1N/KxTriuO95jfsjRxVe8Iu0OJZ7qOQe5yUzoH59LnuvK1Y1Pnb9yvscaGYgPQTHOC02uUpo4Hn3ObGhDlXPPqJP+VjYjxV9yrvu87aG/uWogos8yvscHwsJj5R/jte+glC1Eg/FLDf0P9WwP1LsDOSRMusljRRO1MmCKmcf0TlfV/CjzlLBZeOPCr24tycUCh/lq9YL9arlCO04LfZHiiyLUtQMZMflR/YyC+lGBv/tuwhVzkJG6ftbSKvob7rPu5IqYhQRIBuWWT+0y1TJRhfw04FrKQd01jFPhZSNP2iAbFlm/VBmqaFSBMC3/uixN5SW2hcdtGz8wQIkb2JdVXSx5qnBYmjAD4PaQz2kvKmo9Ht5iNWQvQdIni5Gyo8ruThNl2MG7u2nnucg0U+VECA9llnv0zRPEe1zwDCD5EUOklElP/I87XGlZC8BksuP9FVmvQ+zNJBXUgI/7b/6flVEHz7kPmxebYBUmt4xDZz62gB3+rNRclq0/wCpdP2wmvozwEGDZJwqe14tz0Z2PjDeaYBUeoJhL1M9oLkgmaS6TpLuvGz8zgKkwjPU1dbgB4oJkdJr9t0nZiKXuxg0bx0gFT7F6VgusOsgGaUKy6KkLZftNw6QChssHOyBG2AQQVLbgDpmIRuXRXlygFQ6ZZsl5UeA/QXJRRrAowtPCpBKy48UUXQMGFyI1HioaJqe8PD0WgFS6bG1osoeA4MNkiofa1in/3w0QCp9cCYS9NJyFVBYkMRMJJb/R5X8yNGHPloW5cEAyWt4NZVZdywXKD1Ean2Fxcv7lrV+CJD8F7yuaNbR21OWAD0FScxCalrdiX729O4m+30B8r6iv5S3AgI1B8k41bO//EOI/CNAKlq2iimVY7lAK0FSSyWPz12/e/JDgOQp1afC/wLz5K2AQJshUsszdmfLLYOjlX84KTg8luVHjoUH0KJYiu8+591/PU6LVZZSvblvBnKTyjxeFknnrYDA0GYkJb9P6ST2Qm4DJC9f3RT2A86StwICguQilVcWJVaELpZLWCUl3Dwt1thOhQcwdPmJ8FjWmhb0Y/07/uOosLa6zFOjqdsG4FuIxP7IWfdfT1MZ+yO3k45fCmkfbwUE+HmQRHjMSilse+gZSCxRxVLVS+EBsHaQTNNiWevykD/HoWYg3goIsF2IRD960c1GpulAZVEOMQOJ0DgWHgA7CZJ5rOKkxf7IvNUZyCw5lgvQV5BEH3u8z7Io+5iBRCK+dCwXYC9BcrvKkxarPVXPQP5ReAuAvYTI7WsuutnI/6UeC+T2PQNRfgSgUUeaAAABAoAAAUCAACBAAECAACBAABAgAAgQAAQIAAgQAAQIAAIEAAECgAABAAECgAABQIAAIEAAECAAIEAAECAACBAABAgAAgQABAgAAgQAAQKAAAFAgACAAAFAgAAgQAAQIAAIEAAQIAAIEAAECAACBAAECAACBAABAoAAAUCAAIAAAUCAACBAABAgAAgQABAgAAgQAAQIAAIEAAECAAIEAAECgAABQIAAIEAAQIAAIEAAECAACBDa9utvf7zuPs+1xGCu9/O45lqCn/lFE/BIRzLufrnuPqPuc6pFBuNZ97nqrv+r7tezv//6c6ZJECCsGxyjHBxjrTFocR987O6HWQ6SuSZBgPBQcMTIM5Yu3mgNVsRA4qa7Py67X992QfJFkxDsgbAMj0l0EsKDR7zJQTLRFJiBsNznuOo+NslZR8xSr/P+yLn9EQHCMINjlIPjhdZgAzHgiP2RDzlI5ppEgNB+cCz3OV7l0SRsIwYg4+6+epfsjwyOPZBhhcek++VTWqxlCw925Vm+pz7ZHzEDob3giOWGWK4aaw16NEqL/ZHf02JZ67MmESDUGxzPcnAYFbJP4zwbmeYgsazVKEtY7YbHRVocyxUeHErcezf5XsQMhAqCI0Z/y/IjcGi3+yMry1ofNIkAobzgGCXlRyhX3J/vlUURIJQVHMsTMKqnUoMY4MSy1tvu10v7I3WzB1J3eERo3AgPKvQ6B4l71wyEPQdHjOKUH6F2y7Lxy/2RmSYRIPQXHKOk/AjtURZFgNBjcCizzhDEwOiFsvH1sAdSfnhM0vfyIzAEyqKYgbBlcIzzF2msNRigUfpeFuXS/ogAYb3gGOXgMPqCxQBqnMuiXNofKYslrLLC4yItlquEB/xTfCc+KYtiBsKPwRGbh1dJ+RF4jLIoAoSV4FBmHZ4uBlrLsijKxguQwQWH8iOwvRh4fVIW5XDsgew/PJQfgd1SFsUMpPngiNGSMuvQj2VZlFdpUe13pkkESAvBMUrKrMO+xPfto7LxAqT24FB+BA4nBmw3yqL0yx5IP+ExSYt9DuEBh/UmB8lEU5iBlB4cMepRZh3KEqsB13l/RNl4AVJccIyS8iNQumXZ+GlSFkWAFBAcy32OV3mUA5QvBnpRNv5dsj+yFXsgm4dHlB9ZllkXHlCX5cO8ysabgew1OJQfgXaM0vey8cqiCJDeCY9hDBTG+VqnZON1CJbX+1RTCBDYNDhG6cf3zntfNwgQeDA4fnYgIgJlbOMVvrOJjvD453vnHzsQYeMVzEBgq/fOj5L3dYMAYZDBcVu5NW3/4GcEz/J93eeWtRgaS1gMLTwu0qJO2WSHv238Xjfe140ZCLQZHH2/d977uhEg0FhwRGDs830s8ee99z4KBAjUGxyHfu98BNaN93XTMnsgtBgeJb133vu6MQOBCoIjRv0lvnfe+7oRIFBocERg3C0/UqL4OZVFQYBAAcFR63vnI+heeF83tbMHQq3hMUn1v3fe+7oxA4E9Bsc4tfXeee/rRoBAz8ExSm2/d977uhEgsOPgGNp75yMgva+bKtgDoeTwGOp751fLxr9wJ2AGAusHh/fOL4zS97Io3teNAIFHgmNXZdZbM86zkWlSNp6CWMKilPC4SLsvs96aaBtl4zEDgRwcMbousfxIqVbLxiuLggBhkMExSvsts96aaL+PysYjQBhScBy6zHprIoCVjecg7IGwz/Aoqcx6a5SNxwyEJoMjRsktlR8p1bJs/PK1ujNNggCh1uAYpTrKrLdmWRZF2XgECNUFR61l1lujbDy9sgfCrsNjkuovs96aZVmUiabADIQSg2OcO6qx1ijSKC3Kxsf+yKX9EQQIJQTHKLVdZr01EfBjZePZBUtYbBMeF2lRLVd41Ceu2SdlUTADYd/BEZuzcbpqpDWqtloWJU5rfdAkCBD6Cg5l1tsUA4FvZeM1BwKEXbNB3r64vrEkOdMUrMMeCE/pXHCtQYAAIEAAECAACBAABAgACBAABAgAAgQAAQKAAAEAAQKAAAFAgAAgQAAQIAAgQAAQIAAIEAAECAACBAAECAACBAABAoAAAUCAAIAAAUCAACBAABAgAAgQABAgAAgQAAQIAAIEAATIhuaaYDC+5A++2wiQ7f39159n3S9nbrbmve0+x/nzVnM0Hxxn+buNAOk9RKbdLyfd51JrNGcWodFd4/Pu8yV/znOQzDRPc+I7fJK/0zzRL5pg4xCJpY2LX3/7I268q+7zQqtUPwqN0PjwwPWO//20u94v8vUeabKqfcjX20qCADlokMQN+LLrWMa5Y3muVaoSA4F33XW8WPN6R8fzobve8e+/6j7PNGFVPufgMJvcAUtYuwuSWfeJZa2zZOO1FjF7PF43PO5c7/j/HOffgzoGChEcJ8JDgJQcJNNk47V00YGcxqZpXorc9Fp/yRuvp8n+SMne5oGC7+SOWcLqJ0RuRzu//vbHu+7X6+4z1ipFmHefy11vmOYR7ay73pPu1zfJ/khJA4Uz+xxmILUGybz7nOYRqpv4cCLQez9tc+d0nmXMww4UYoZ5KjzMQFoIkhgJHdt4PYi9nrZxOu/gA4V3m+xpYQZSQ5DEjW3jdT8+51Hoy0OMQvPs82WefX52OXoX36lj4WEG0nqIxCjpLO+PxAh1rFV2Pgo9L+XBsDz7PMn7I1dmnzs3y9dbSJuBDCpIPuf9kRilzrXITlzmUei0wOs9zbNP1Qt2I74zL/M+h/AQIIMNklijt/G6/Sj0dvlim2O5+5h9rixjzly2jWeYywMRHzTHYVnCKqRjSd83XuMY6ESrrD0KPavtwbCVsijjtDjmPXIp1xLfj0snqwQID3cssT/ynxwkY63y8Ci09gfDVk7nvc7X2/7I/ZQfKZQlrEI7lrw/oizKj5p7qjj/XZzOu3+gcKb8iABhs45lmmy8LkUHcrIss97gtV6WRTlJ9kdSKvhABN9ZwqqgY0nDfjBtnh4ps97g9b59fmXAZeOVWRcg9NCxxBfq5YA2Xgf9VPEAy8bH/X1mqaoulrDq61hifySWtc5Tu/sjMds68VTxt+oFJ6nd/ZHlg5/HwkOAsL+OZbnx2lKJ6uhAlmXW567y99lno2XjlVmvnCWsujuWVsrGR1hc2jD9+ewztVE2Pv4eBglmIBQ0Qq21LErvZdYbvN7RVsvqBbUNFF4qs24GQpkdS00br07bbD/7rOV0njLrZiBU1LnEF7XUB9MOWma90dlnyWXj4x5UZt0MhApHqCWVjS+qzHqD13uWyiobP0vKrJuBUH3H8nmlLMqhRvzL0zbCo//rPU2HPZ0X99iZMusChPY6ln2XjZ/l4Giy/EjJs89o87TfsvF7ee88ZbGENbCOJe2nbPxyFDrT6ge93nEd9lE23oEIAcLAOpZl2fhYL3++w1Go0zblXe/bmWAPp/OUWR84S1gD71iiVHbaTdn4mNU4bVP29Y5rs4vTecqsI0D41rFM0+Zl46MDOcnlR+xzlH+tty0br8x6Xf6rz9/8X1+/fk15jfRjT3/GLClbUI3uXhil9cqixPU8917q6q/3umXjfY/ruq7P8nWd9NWvx0m7fcxAoiO66f5CV/kvRdkj1GVZlNN0/7Hf1dM2wqP+6/0hPX46L+6BU+VHqgqPi+hzewyPvc5Afuh8VN+s6mZcfV/3NF8/HUm7s8/l6Tzf1XZnkzubgew7QFZHNY551jUdHnkwbDDXO07lze1pVRX8+67GfdAAWXJ+HGDzgV3MGF8f4I/f2x7IY2LKFfsjF/ZHANYOjwiNmwOFxzelHON9k4Nk4tYAeDA4xt3nUyqjYGZRT6JHY1x3jfN7WmzezdwuAN/2OUp678t8NUDmBbXVOD65XpMTP8CQgyMG1suTkCX53/iP2030/IPGetqosB/ytrZS93nrRAgwsPCY5OAYFfjjxXNgn1f3QP5T4A+5PGXwKZ9xBmg9OGIFJk7F9llBeRvz5ZH+1T2QaSr3PdrRiO+7Rp0lbzkD2gyOUer3NQu78q1m3rclrPwXuEjlrbXdZ5qDxLIW0EJ4XBQ8gF/1OVfw/jFA8l/kfSpnp/8x3j0B1B4c+yw/sos+9x+vKr4vQG6P01YSImGelEUB6gqO5zk4xpX8yD+Ex70BUuGUammWlJsGyg6OQ5Yf2aZvfXnflsGDAZL/sqNU1sMr64jqoZf2R4DCwmO1snUN5uknqzuPBsjKX3ycdvvu7H1Mt5SiBkoIjug/Sz2S+1D/udb+8loBUnGCxnrduf0R4ADBMUr1reBM0xNOuD4pQHKj1LiGp2w8sK/gKLX8yGNmaYNn7J4cIHfSdd8vMdlWPACjLArQV3hMUiGVctc0z8Gx0eupNw6QlQar6RzzssFif2Tqdgd2FBzjPOOoZUC9k+fotg6QlQaMH6S2Y7/KxgPb9HujVEf5kVXTtKNK5zsLkNyYz/JsZJCNCQwmOJb7HIMeNO80QIY+nQMGER6W7fsMkJWGnqRy69k/1NAbbygBTQdHjeVHen2fUq8Bcmeq1/yRNqDJ4KhxaX4vjy70HiArF2GUGn+oBmguPDw8XUKArFyQcVIWBSg7OKKfqq38yPm+H0/Ye4BUnOwxFVQ2HtoOjlHygHT5AZIvVq2ljZWNh7aCo8a92oOXaDpogFSe+ueWtaCJ8Jik+sqPFLEa8ksJrZET9LSy89VmIFB/eHxMdR3LLWo/9qik1onnL7rPcVqs6ZV88mnmWRFowv9U8nNGaByXtupRxBLWAyODks9eH9sDgWZmITep3FWPWSp4z7XYAFm5uDG9LKksSpx2OPe1g2YCJJbO3xf2Y81TBac+iw+QlYs8SYff6PqSZx8eLIS2QqSUvZCqnjs7quUC5wdklvsjh+KpdGhTCasK01TgPkcTM5A7o4VR2n9ZlNg4P/U9g2ZnIdGnHOKZtFmqtPZelQGycsFjyrmvcgOnnkKHpgMklsdjQ31fy+TzVHn176oDZOXC910WZdpd5DNfMWg+RKIvuer5j2nm/UNHLVz0vGYY+yNve7rYTl3BAOS+ZN7jHzFNi32Oixbaq4kZyJ0RxK5f+nLpTYUwqFlI9B0fd/zbztKOXycrQPq9CXZRFmWen4wHhhUiuzrWO089vU62BEet3gA7Koti3wOGadvv/pfc95y0Gh5Nz0DujCZiFhKb7JOnTDkd24VBz0Iu0mbl3ad51jFvvY0GESArN8Q4rV8WRb0rGHaAPPVY715fJytADndjxEzksbIoNs6BZV9x/ZN/7SCvky3B0RBvip+URYlZhxdFAcu+4rEnxKMPOR5ieAx2BnJnhDFK/yyLcjbUmwG4t48Ypx+P9R78dbICpLyb5HdPnAP39A+xjDVJA9znECAA2wXIqPtlbHVCgACwA0eaAIBN/L8AAwBLF+9LrWiboAAAAABJRU5ErkJggg=="
decoded = image_decoder_b64(encode_weaviate_logo_b64)
with open("integration/weaviate-logo.png", "rb") as file:
self.assertEqual(decoded, file.read())
self.assertIsInstance(decoded, bytes)
def test_file_encoder_b64(self):
"""Test the `image_encoder_b64` function."""
encode_weaviate_logo_b64 = "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"
file_path_value_error_message = "No file found at location non-existingfile.png"
type_error_message = (
'"image_or_image_path" should be a image path or a binary read file (io.BufferedReader)'
)
# invalid calls
with self.assertRaises(ValueError) as error:
image_encoder_b64("non-existingfile.png")
check_error_message(self, error, file_path_value_error_message)
with self.assertRaises(TypeError) as error:
image_encoder_b64(True)
check_error_message(self, error, type_error_message)
with self.assertRaises(TypeError) as error:
with open("image.png", "wb") as file:
image_encoder_b64(file)
check_error_message(self, error, type_error_message)
# valid calls
encrypted_1 = image_encoder_b64("integration/weaviate-logo.png")
self.assertEqual(encrypted_1, encode_weaviate_logo_b64)
self.assertIsInstance(encrypted_1, str)
with open("integration/weaviate-logo.png", "rb") as file:
encrypted_2 = image_encoder_b64(file)
self.assertEqual(encrypted_2, encode_weaviate_logo_b64)
self.assertIsInstance(encrypted_2, str)
def test_file_decoder_b64(self):
"""Test the `file_decoder_b64` function."""
encode_weaviate_logo_b64 = "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"
decoded = image_decoder_b64(encode_weaviate_logo_b64)
with open("integration/weaviate-logo.png", "rb") as file:
self.assertEqual(decoded, file.read())
self.assertIsInstance(decoded, bytes)
@patch("weaviate.util.uuid_lib")
def test_generate_uuid5(self, mock_uuid):
result = generate_uuid5("TestID!", "Test!")
self.assertIsInstance(result, str)
mock_uuid.uuid5.assert_called()
def test_parse_version_string(self):
assert parse_version_string("v1.18.1") == parse_version_string("1.18.1")
assert parse_version_string("v1.18.1") == parse_version_string("1.18.5")
assert len(parse_version_string("v1.18.1")) == 2
assert len(parse_version_string("1.1.5")) == 2
assert len(parse_version_string("2")) == 2
assert parse_version_string("v1.18.3") == (1, 18)
assert parse_version_string("1.1.5") == (1, 1)
assert parse_version_string("2") == (2, 0)
assert parse_version_string("2.0.0") == (2, 0)
assert parse_version_string("v2.05.5") == (2, 5)
MINIMUM_NO_WARNING_VERSION_MAJOR, MINIMUM_NO_WARNING_VERSION_MINOR = parse_version_string(
MINIMUM_NO_WARNING_VERSION
)
MINIMUM_NO_WARNING_VERSION_MAJOR = int(MINIMUM_NO_WARNING_VERSION_MAJOR)
MINIMUM_NO_WARNING_VERSION_MINOR = int(MINIMUM_NO_WARNING_VERSION_MINOR)
@pytest.mark.parametrize(
"version,too_old",
[
(f"v{MINIMUM_NO_WARNING_VERSION_MAJOR - 1}.{MINIMUM_NO_WARNING_VERSION_MINOR - 1}.1", True),
(f"v{MINIMUM_NO_WARNING_VERSION_MAJOR - 1}.{MINIMUM_NO_WARNING_VERSION_MINOR + 1}.2", True),
(f"v{MINIMUM_NO_WARNING_VERSION_MAJOR}.{MINIMUM_NO_WARNING_VERSION_MINOR - 1}.3", True),
(MINIMUM_NO_WARNING_VERSION, False),
(f"v{MINIMUM_NO_WARNING_VERSION_MAJOR}.{MINIMUM_NO_WARNING_VERSION_MINOR + 1}.0", False),
(
f"v{MINIMUM_NO_WARNING_VERSION_MAJOR + 1}.{MINIMUM_NO_WARNING_VERSION_MINOR - 1}.0",
False,
),
(
f"v{MINIMUM_NO_WARNING_VERSION_MAJOR + 1}.{MINIMUM_NO_WARNING_VERSION_MINOR + 1}.0",
False,
),
],
)
def test_is_weaviate_too_old(version: str, too_old: bool):
assert is_weaviate_too_old(version) is too_old
@pytest.mark.parametrize(
"input_str,expected",
[
# Test parsing with microseconds and Z timezone
(
"2023-01-15T14:30:45.123456Z",
datetime(2023, 1, 15, 14, 30, 45, 123456, tzinfo=timezone.utc),
),
# Test parsing without microseconds
(
"2023-01-15T14:30:45Z",
datetime(2023, 1, 15, 14, 30, 45, 0, tzinfo=timezone.utc),
),
# Test parsing with offset timezone
(
"2023-01-15T14:30:45.123456+02:00",
datetime(2023, 1, 15, 14, 30, 45, 123456, tzinfo=timezone(timedelta(hours=2))),
),
# Test truncating excess microseconds
(
"2023-01-15T14:30:45.123456789Z",
datetime(2023, 1, 15, 14, 30, 45, 123456, tzinfo=timezone.utc),
),
# Test handling year 0 (should return datetime.min)
("0000-01-15T14:30:45.123456Z", datetime.min),
# Test empty string (protobuf default for unset string field)
("", datetime.min),
],
)
def test_datetime_from_weaviate_str(input_str: str, expected: datetime) -> None:
assert _datetime_from_weaviate_str(input_str) == expected
@pytest.mark.parametrize(
"current_version,latest_version,too_old",
[
("v1.18.1", "v1.18.1", False),
("v1.0.0", "v1.3.1", False),
("v1.0.0", "v1.4.0", True),
("unknown", "v1.4.0", False),
("v1.4.0", "unknown", False),
("v0.4.0", "v1.16.16", True),
("v0.4.0", "v1.4.0", True),
("v0.4.0", "v1.0.0", True),
],
)
def test_is_weaviate_client_too_old(current_version: str, latest_version: str, too_old: bool):
assert is_weaviate_client_too_old(current_version, latest_version) is too_old
@pytest.mark.parametrize(
"in_str, out_str",
[
('"', '\\"'),
('"', '\\"'),
('\\"', '\\"'),
('\\"', '\\"'),
('\\\\"', '\\\\\\"'),
('\\\\"', '\\\\\\"'),
('\\\\\\"', '\\\\\\"'),
],
)
def test_sanitize_str(in_str: str, out_str: str) -> None:
assert _sanitize_str(in_str) == f'"{out_str}"'