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Fix test infra, sentence embeddings mixin

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This commit is contained in:
Ashwin Bharambe 2025-02-21 15:10:10 -08:00
parent 182608d4bf
commit e7d261ef4a
4 changed files with 12 additions and 34 deletions
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22
tests/client-sdk/vector_io/conftest.py
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@ -1,22 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
def pytest_addoption(parser):
parser.addoption(
"--embedding-model",
action="store",
default="all-MiniLM-L6-v2",
help="Specify the embedding model to use for testing",
)
def pytest_generate_tests(metafunc):
if "embedding_model" in metafunc.fixturenames:
metafunc.parametrize(
"embedding_model",
[metafunc.config.getoption("--embedding-model")],
)

10
tests/client-sdk/vector_io/test_vector_io.py
View file

@ -36,12 +36,12 @@ def single_entry_vector_db_registry(llama_stack_client, empty_vector_db_registry
@pytest.mark.parametrize("provider_id", INLINE_VECTOR_DB_PROVIDERS)
def test_vector_db_retrieve(llama_stack_client, embedding_model, empty_vector_db_registry, provider_id):
def test_vector_db_retrieve(llama_stack_client, embedding_model_id, empty_vector_db_registry, provider_id):
# Register a memory bank first
vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
llama_stack_client.vector_dbs.register(
vector_db_id=vector_db_id,
embedding_model=embedding_model,
embedding_model=embedding_model_id,
embedding_dimension=384,
provider_id=provider_id,
)
@ -50,7 +50,7 @@ def test_vector_db_retrieve(llama_stack_client, embedding_model, empty_vector_db
response = llama_stack_client.vector_dbs.retrieve(vector_db_id=vector_db_id)
assert response is not None
assert response.identifier == vector_db_id
assert response.embedding_model == embedding_model
assert response.embedding_model == embedding_model_id
assert response.provider_id == provider_id
assert response.provider_resource_id == vector_db_id
@ -61,11 +61,11 @@ def test_vector_db_list(llama_stack_client, empty_vector_db_registry):
@pytest.mark.parametrize("provider_id", INLINE_VECTOR_DB_PROVIDERS)
def test_vector_db_register(llama_stack_client, embedding_model, empty_vector_db_registry, provider_id):
def test_vector_db_register(llama_stack_client, embedding_model_id, empty_vector_db_registry, provider_id):
vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
llama_stack_client.vector_dbs.register(
vector_db_id=vector_db_id,
embedding_model=embedding_model,
embedding_model=embedding_model_id,
embedding_dimension=384,
provider_id=provider_id,
)