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# The MIT License (MIT)
# Copyright (c) Microsoft Corporation. All rights reserved.
import unittest
import uuid
import pytest
import azure.cosmos.exceptions as exceptions
import test_config
from azure.cosmos import CosmosClient, PartitionKey
@pytest.mark.cosmosSearchQuery
class TestVectorPolicy(unittest.TestCase):
client: CosmosClient = None
host = test_config.TestConfig.host
masterKey = test_config.TestConfig.masterKey
connectionPolicy = test_config.TestConfig.connectionPolicy
@classmethod
def setUpClass(cls):
if (cls.masterKey == '[YOUR_KEY_HERE]' or
cls.host == '[YOUR_ENDPOINT_HERE]'):
raise Exception(
"You must specify your Azure Cosmos account values for "
"'masterKey' and 'host' at the top of this class to run the "
"tests.")
cls.client = CosmosClient(cls.host, cls.masterKey)
cls.created_database = cls.client.get_database_client(test_config.TestConfig.TEST_DATABASE_ID)
cls.test_db = cls.client.create_database(str(uuid.uuid4()))
@classmethod
def tearDownClass(cls):
test_config.TestConfig.try_delete_database_with_id(cls.client, cls.test_db.id)
def test_create_vector_embedding_container(self):
indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "flat"},
{"path": "/vector2", "type": "quantizedFlat", "quantizationByteSize": 8},
{"path": "/vector3", "type": "diskANN", "quantizationByteSize": 8, "vectorIndexShardKey": ["/city"], "indexingSearchListSize": 50}
]
}
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float32",
"dimensions": 256,
"distanceFunction": "euclidean"
},
{
"path": "/vector2",
"dataType": "int8",
"dimensions": 200,
"distanceFunction": "dotproduct"
},
{
"path": "/vector3",
"dataType": "uint8",
"dimensions": 400,
"distanceFunction": "cosine"
}
]
}
container_id = "vector_container" + str(uuid.uuid4())
created_container = self.test_db.create_container(
id=container_id,
partition_key=PartitionKey(path="/id"),
vector_embedding_policy=vector_embedding_policy,
indexing_policy=indexing_policy
)
properties = created_container.read()
assert properties["vectorEmbeddingPolicy"] == vector_embedding_policy
assert properties["indexingPolicy"]["vectorIndexes"] == indexing_policy["vectorIndexes"]
self.test_db.delete_container(container_id)
# Pass a vector indexing policy with hierarchical vectorIndexShardKey value
indexing_policy = {
"vectorIndexes": [
{"path": "/vector2", "type": "diskANN", 'quantizationByteSize': 64, 'indexingSearchListSize': 100, "vectorIndexShardKey": ["/country/city"]}]
}
container_id = "vector_container" + str(uuid.uuid4())
created_container = self.test_db.create_container(
id=container_id,
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
properties = created_container.read()
assert properties["vectorEmbeddingPolicy"] == vector_embedding_policy
assert properties["indexingPolicy"]["vectorIndexes"] == indexing_policy["vectorIndexes"]
self.test_db.delete_container(container_id)
def test_fail_create_vector_indexing_policy(self):
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float32",
"dimensions": 256,
"distanceFunction": "euclidean"
},
{
"path": "/vector2",
"dataType": "int8",
"dimensions": 200,
"distanceFunction": "dotproduct"
}
]
}
# Pass a vector indexing policy without embedding policy
indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "flat"}]
}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy
)
pytest.fail("Container creation should have failed for lack of embedding policy.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "vector1 not matching in Embedding's path" in e.http_error_message
# Pass a vector indexing policy with an invalid type
indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "notFlat"}]
}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
pytest.fail("Container creation should have failed for wrong index type.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "Index Type::notFlat is invalid" in e.http_error_message
# Pass a vector indexing policy with non-matching path
indexing_policy = {
"vectorIndexes": [
{"path": "/vector3", "type": "flat"}]
}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
pytest.fail("Container creation should have failed for index mismatch.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "vector3 not matching in Embedding's path" in e.http_error_message
# Pass a vector indexing policy with wrong quantizationByteSize value
indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "quantizedFlat", "quantizationByteSize": 0}]
}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
pytest.fail("Container creation should have failed for value mismatch.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "The Vector Indexing Policy parameter QuantizationByteSize value :: 0 is out of range. The allowed range is between 1 and 256."\
in e.http_error_message
# Pass a vector indexing policy with wrong indexingSearchListSize value
indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "diskANN", "indexingSearchListSize": 5}]
}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
pytest.fail("Container creation should have failed for value mismatch.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "IndexingSearchListSize value :: 5 is out of range. The allowed range is between 25 and 500."\
in e.http_error_message
# Pass a vector indexing policy with wrong vectorIndexShardKey value
indexing_policy = {
"vectorIndexes": [
{"path": "/vector2", "type": "diskANN", "vectorIndexShardKey": ["country"]}]
}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
pytest.fail("Container creation should have failed for value mismatch.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "The Vector Indexing Policy has an invalid Shard Path: country." in e.http_error_message
# Pass a vector indexing policy with too many shard paths
indexing_policy = {
"vectorIndexes": [
{"path": "/vector2", "type": "diskANN", "vectorIndexShardKey": ["/country", "/city", "/zipcode"]}]
}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
pytest.fail("Container creation should have failed for value mismatch.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "The number of shard paths defined in the Vector Indexing Policy: 3 exceeds the maximum: 1." \
in e.http_error_message
# Pass a vector indexing policy with an invalid type for vectorIndexShardKey
indexing_policy = {
"vectorIndexes": [
{"path": "/vector2", "type": "diskANN", "vectorIndexShardKey": "/country"}]
}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
pytest.fail("Container creation should have failed for value mismatch.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "One of the specified inputs is invalid" \
in e.http_error_message
# Pass a vector indexing policy with dimensions above 512 to test the max range of "quantizationByteSize"
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float32",
"dimensions": 550,
"distanceFunction": "euclidean"
}]}
indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "quantizedFlat", "quantizationByteSize": 513 }]
}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
pytest.fail("Container creation should have failed for value mismatch.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "The Vector Indexing Policy parameter QuantizationByteSize value :: 513 is out of range." \
in e.http_error_message
def test_fail_replace_vector_indexing_policy(self):
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float32",
"dimensions": 256,
"distanceFunction": "euclidean"
}]}
indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "flat"}]
}
container_id = "vector_container" + str(uuid.uuid4())
created_container = self.test_db.create_container(
id=container_id,
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
new_indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "quantizedFlat"}]
}
try:
self.test_db.replace_container(
created_container,
PartitionKey(path="/id"),
indexing_policy=new_indexing_policy)
pytest.fail("Container replace should have failed for indexing policy.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert ("The Vector Indexing Policy's path::/vector1 not matching in Embedding's path."
in e.http_error_message)
self.test_db.delete_container(container_id)
def test_fail_create_vector_embedding_policy(self):
# Using invalid data type
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float33",
"dimensions": 256,
"distanceFunction": "euclidean"
}]}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
vector_embedding_policy=vector_embedding_policy)
pytest.fail("Container creation should have failed but succeeded.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "Vector Embedding Policy has an invalid DataType" in e.http_error_message
# Using too many dimensions
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float32",
"dimensions": 8000,
"distanceFunction": "euclidean"
}]}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
vector_embedding_policy=vector_embedding_policy)
pytest.fail("Container creation should have failed but succeeded.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "Vector Embedding Policy has Dimensions:8000 which is more than the maximum" \
" supported value" in e.http_error_message
# Using negative dimensions
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float32",
"dimensions": -1,
"distanceFunction": "euclidean"
}]}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
vector_embedding_policy=vector_embedding_policy)
pytest.fail("Container creation should have failed but succeeded.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "The Vector Embedding Policy has Dimensions:-1" in e.http_error_message
# Using invalid distance function
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float32",
"dimensions": 256,
"distanceFunction": "handMeasured"
}]}
try:
self.test_db.create_container(
id='vector_container',
partition_key=PartitionKey(path="/id"),
vector_embedding_policy=vector_embedding_policy
)
pytest.fail("Container creation should have failed but succeeded.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert "The Vector Embedding Policy has an invalid DistanceFunction:handMeasured" in e.http_error_message
if __name__ == '__main__':
unittest.main()
|
