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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 as CosmosSyncClient
from azure.cosmos import PartitionKey
from azure.cosmos.aio import CosmosClient
@pytest.mark.cosmosSearchQuery
class TestVectorPolicyAsync(unittest.IsolatedAsyncioTestCase):
host = test_config.TestConfig.host
masterKey = test_config.TestConfig.masterKey
connectionPolicy = test_config.TestConfig.connectionPolicy
client: CosmosClient = None
cosmos_sync_client: CosmosSyncClient = None
TEST_DATABASE_ID = test_config.TestConfig.TEST_DATABASE_ID
@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.cosmos_sync_client = CosmosSyncClient(cls.host, cls.masterKey)
cls.test_db = cls.cosmos_sync_client.create_database(str(uuid.uuid4()))
@classmethod
def tearDownClass(cls):
cls.cosmos_sync_client.delete_database(cls.test_db.id)
async def asyncSetUp(self):
self.client = CosmosClient(self.host, self.masterKey)
self.test_db = self.client.get_database_client(self.test_db.id)
async def asyncTearDown(self):
await self.client.close()
async def test_create_vector_embedding_container_async(self):
indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "flat"},
{"path": "/vector2", "type": "quantizedFlat", "quantizationByteSize": 64, "vectorIndexShardKey": ["/city"]},
{"path": "/vector3", "type": "diskANN", "quantizationByteSize": 8, "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 = await self.test_db.create_container(
id=container_id,
partition_key=PartitionKey(path="/id"),
vector_embedding_policy=vector_embedding_policy,
indexing_policy=indexing_policy
)
properties = await created_container.read()
assert properties["vectorEmbeddingPolicy"] == vector_embedding_policy
assert properties["indexingPolicy"]["vectorIndexes"] == indexing_policy["vectorIndexes"]
await self.test_db.delete_container(container_id)
async def test_replace_vector_indexing_policy_async(self):
# Replace should work so long as the new indexing policy doesn't change the vector indexes, and as long as
# the previously defined vector embedding policy is also provided.
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float32",
"dimensions": 256,
"distanceFunction": "euclidean"
}
]
}
indexing_policy = {
"indexingMode": "consistent",
"automatic": True,
"includedPaths": [
{
"path": "/*"
}
],
"excludedPaths": [
{
"path": "/vector1/*"
},
{
"path": "/\"_etag\"/?"
}
],
"fullTextIndexes": [],
"vectorIndexes": [
{
"path": "/vector1",
"type": "diskANN",
"quantizationByteSize": 128,
"indexingSearchListSize": 100
}
]
}
container_id = "vector_container" + str(uuid.uuid4())
created_container = await 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 = {
"indexingMode": "consistent",
"automatic": True,
"includedPaths": [
{"path": "/color/?"},
{"path": "/description/?"},
{"path": "/cost/?"}
],
"excludedPaths": [
{"path": "/*"},
{"path": "/vector1/*"},
{"path": "/\"_etag\"/?"}
],
"fullTextIndexes": [],
"vectorIndexes": [
{
"path": "/vector1",
"type": "diskANN",
"quantizationByteSize": 128,
"indexingSearchListSize": 100
}]
}
await self.test_db.replace_container(
created_container,
PartitionKey(path="/id"),
vector_embedding_policy=vector_embedding_policy,
indexing_policy=new_indexing_policy)
properties = await created_container.read()
assert properties["vectorEmbeddingPolicy"] == vector_embedding_policy
assert properties["indexingPolicy"]["vectorIndexes"] == indexing_policy["vectorIndexes"]
await self.test_db.delete_container(container_id)
async def test_fail_create_vector_indexing_policy_async(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:
await 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:
await 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:
await 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:
await 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:
await 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:
await 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:
await 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
async def test_fail_replace_vector_indexing_policy_async(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 = await self.test_db.create_container(
id=container_id,
partition_key=PartitionKey(path="/id"),
indexing_policy=indexing_policy,
vector_embedding_policy=vector_embedding_policy
)
# don't provide vector embedding policy
try:
await self.test_db.replace_container(
created_container,
PartitionKey(path="/id"),
indexing_policy=indexing_policy)
pytest.fail("Container replace should have failed for missing embedding 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)
# don't provide vector indexing policy
try:
await self.test_db.replace_container(
created_container,
PartitionKey(path="/id"),
vector_embedding_policy=vector_embedding_policy)
pytest.fail("Container replace should have failed for missing indexing policy.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert ("The Vector Indexing Policy cannot be changed in Collection Replace."
in e.http_error_message)
# using a new indexing policy
new_indexing_policy = {
"vectorIndexes": [
{"path": "/vector1", "type": "quantizedFlat"}]
}
try:
await self.test_db.replace_container(
created_container,
PartitionKey(path="/id"),
vector_embedding_policy=vector_embedding_policy,
indexing_policy=new_indexing_policy)
pytest.fail("Container replace should have failed for new indexing policy.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert ("Paths in existing vector indexing policy cannot be modified in Collection Replace"
in e.http_error_message)
# using a new vector embedding policy
new_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float32",
"dimensions": 384,
"distanceFunction": "euclidean"}]}
try:
await self.test_db.replace_container(
created_container,
PartitionKey(path="/id"),
vector_embedding_policy=new_embedding_policy,
indexing_policy=indexing_policy)
pytest.fail("Container replace should have failed for new embedding policy.")
except exceptions.CosmosHttpResponseError as e:
assert e.status_code == 400
assert ("The Vector Embedding Policy cannot be changed in Collection Replace"
in e.http_error_message)
await self.test_db.delete_container(container_id)
async def test_fail_create_vector_embedding_policy_async(self):
# Using invalid data type
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/vector1",
"dataType": "float33",
"dimensions": 256,
"distanceFunction": "euclidean"
}]}
try:
await 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:
await 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:
await 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:
await 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()
|
