forked from phoenix-oss/llama-stack-mirror
[client sdk test] add options for inference_model, safety_shield, embedding_model (#843)
# What does this PR do? Default inference_model for testing: "meta-llama/Llama-3.1-8B-Instruct" Default vision inference_model for testing: "meta-llama/Llama-3.2-11B-Vision-Instruct" ## Test Plan `/opt/miniconda3/envs/stack/bin/pytest -s -v --inference-model=meta-llama/Llama-3.2-3B-Instruct tests/client-sdk/agents` `/opt/miniconda3/envs/stack/bin/pytest -s -v --embedding-model=all-MiniLM-L6-v2 tests/client-sdk/vector_io` `/opt/miniconda3/envs/stack/bin/pytest -s -v --safety-shield=meta-llama/Llama-Guard-3-1B tests/client-sdk/safety` ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
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7 changed files with 84 additions and 83 deletions
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@ -79,18 +79,6 @@ class TestClientTool(ClientTool):
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return -1
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@pytest.fixture(scope="session")
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def text_model_id(llama_stack_client):
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available_models = [
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model.identifier
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for model in llama_stack_client.models.list()
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if model.identifier.startswith("meta-llama") and "405" not in model.identifier
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]
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model_id = available_models[0]
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print(f"Using model: {model_id}")
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return model_id
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@pytest.fixture(scope="session")
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def agent_config(llama_stack_client, text_model_id):
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available_shields = [
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@ -20,6 +20,10 @@ def pytest_configure(config):
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config.pluginmanager.register(Report())
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TEXT_MODEL = "meta-llama/Llama-3.1-8B-Instruct"
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VISION_MODEL = "meta-llama/Llama-3.2-11B-Vision-Instruct"
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def pytest_addoption(parser):
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parser.addoption(
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"--report",
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@ -27,10 +31,18 @@ def pytest_addoption(parser):
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action="store_true",
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help="Knob to determine if we should generate report, e.g. --output=True",
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)
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TEXT_MODEL = "meta-llama/Llama-3.1-8B-Instruct"
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INFERENCE_MODEL = "meta-llama/Llama-3.2-11B-Vision-Instruct"
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parser.addoption(
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"--inference-model",
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action="store",
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default=TEXT_MODEL,
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help="Specify the inference model to use for testing",
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)
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parser.addoption(
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"--vision-inference-model",
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action="store",
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default=VISION_MODEL,
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help="Specify the vision inference model to use for testing",
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)
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@pytest.fixture(scope="session")
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@ -61,3 +73,18 @@ def llama_stack_client(provider_data):
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else:
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raise ValueError("LLAMA_STACK_CONFIG or LLAMA_STACK_BASE_URL must be set")
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return client
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def pytest_generate_tests(metafunc):
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if "text_model_id" in metafunc.fixturenames:
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metafunc.parametrize(
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"text_model_id",
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[metafunc.config.getoption("--inference-model")],
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scope="session",
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)
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if "vision_model_id" in metafunc.fixturenames:
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metafunc.parametrize(
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"vision_model_id",
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[metafunc.config.getoption("--vision-inference-model")],
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scope="session",
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)
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@ -34,30 +34,6 @@ def inference_provider_type(llama_stack_client):
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return inference_providers[0].provider_type
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@pytest.fixture(scope="session")
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def text_model_id(llama_stack_client):
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available_models = [
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model.identifier
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for model in llama_stack_client.models.list()
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if model.identifier.startswith("meta-llama") and "405" not in model.identifier
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]
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assert len(available_models) > 0
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return available_models[0]
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@pytest.fixture(scope="session")
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def vision_model_id(llama_stack_client):
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available_models = [
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model.identifier
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for model in llama_stack_client.models.list()
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if "vision" in model.identifier.lower()
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]
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if len(available_models) == 0:
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pytest.skip("No vision models available")
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return available_models[0]
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@pytest.fixture
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def get_weather_tool_definition():
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return {
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@ -107,6 +83,7 @@ def test_text_completion_streaming(llama_stack_client, text_model_id):
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assert "blue" in "".join(streamed_content).lower().strip()
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@pytest.mark.skip("Most inference providers don't support log probs yet")
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def test_completion_log_probs_non_streaming(llama_stack_client, text_model_id):
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response = llama_stack_client.inference.completion(
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content="Complete the sentence: Micheael Jordan is born in ",
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@ -124,6 +101,7 @@ def test_completion_log_probs_non_streaming(llama_stack_client, text_model_id):
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assert all(len(logprob.logprobs_by_token) == 3 for logprob in response.logprobs)
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@pytest.mark.skip("Most inference providers don't support log probs yet")
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def test_completion_log_probs_streaming(llama_stack_client, text_model_id):
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response = llama_stack_client.inference.completion(
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content="Complete the sentence: Micheael Jordan is born in ",
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22
tests/client-sdk/safety/conftest.py
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22
tests/client-sdk/safety/conftest.py
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@ -0,0 +1,22 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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def pytest_addoption(parser):
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parser.addoption(
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"--safety_shield",
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action="store",
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default="meta-llama/Llama-Guard-3-1B",
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help="Specify the safety shield model to use for testing",
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)
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def pytest_generate_tests(metafunc):
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if "llama_guard_text_shield_id" in metafunc.fixturenames:
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metafunc.parametrize(
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"llama_guard_text_shield_id",
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[metafunc.config.getoption("--safety_shield")],
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)
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@ -32,16 +32,6 @@ def available_shields(llama_stack_client):
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return [shield.identifier for shield in llama_stack_client.shields.list()]
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@pytest.fixture(scope="session")
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def llama_guard_text_shield_id(available_shields):
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if "meta-llama/Llama-Guard-3-1B" in available_shields:
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return "meta-llama/Llama-Guard-3-1B"
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elif "meta-llama/Llama-Guard-3-8B" in available_shields:
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return "meta-llama/Llama-Guard-3-8B"
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else:
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pytest.skip("Llama-Guard shield is not available. Skipping.")
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@pytest.fixture(scope="session")
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def code_scanner_shield_id(available_shields):
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if "CodeScanner" in available_shields:
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22
tests/client-sdk/vector_io/conftest.py
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tests/client-sdk/vector_io/conftest.py
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@ -0,0 +1,22 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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def pytest_addoption(parser):
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parser.addoption(
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"--embedding-model",
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action="store",
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default="all-MiniLM-L6-v2",
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help="Specify the embedding model to use for testing",
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)
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def pytest_generate_tests(metafunc):
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if "embedding_model" in metafunc.fixturenames:
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metafunc.parametrize(
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"embedding_model",
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[metafunc.config.getoption("--embedding-model")],
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)
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@ -6,39 +6,13 @@
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import random
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import pytest
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@pytest.fixture(scope="function")
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def empty_vector_db_registry(llama_stack_client):
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vector_dbs = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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for vector_db_id in vector_dbs:
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llama_stack_client.vector_dbs.unregister(vector_db_id=vector_db_id)
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@pytest.fixture(scope="function")
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def single_entry_vector_db_registry(llama_stack_client, empty_vector_db_registry):
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vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
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llama_stack_client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model="all-MiniLM-L6-v2",
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embedding_dimension=384,
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provider_id="faiss",
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)
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vector_dbs = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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return vector_dbs
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def test_vector_db_retrieve(llama_stack_client, empty_vector_db_registry):
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def test_vector_db_retrieve(llama_stack_client, embedding_model):
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# Register a memory bank first
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vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
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llama_stack_client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model="all-MiniLM-L6-v2",
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embedding_model=embedding_model,
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embedding_dimension=384,
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provider_id="faiss",
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)
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response = llama_stack_client.vector_dbs.retrieve(vector_db_id=vector_db_id)
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assert response is not None
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assert response.identifier == vector_db_id
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assert response.embedding_model == "all-MiniLM-L6-v2"
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assert response.embedding_model == embedding_model
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assert response.provider_id == "faiss"
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assert response.provider_resource_id == vector_db_id
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def test_vector_db_list(llama_stack_client, empty_vector_db_registry):
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def test_vector_db_list(llama_stack_client):
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vector_dbs_after_register = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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assert len(vector_dbs_after_register) == 0
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def test_vector_db_register(llama_stack_client, empty_vector_db_registry):
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def test_vector_db_register(llama_stack_client, embedding_model):
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vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
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llama_stack_client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model="all-MiniLM-L6-v2",
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embedding_model=embedding_model,
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embedding_dimension=384,
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provider_id="faiss",
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)
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assert vector_dbs_after_register == [vector_db_id]
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def test_vector_db_unregister(llama_stack_client, single_entry_vector_db_registry):
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def test_vector_db_unregister(llama_stack_client):
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vector_dbs = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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