mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-15 20:29:29 +00:00
Merge remote-tracking branch 'origin/main' into dependabot/uv/openai-2.5.0
This commit is contained in:
Ashwin Bharambe
commit
13450c1a68
317 changed files with 86802 additions and 18957 deletions
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@ -26,6 +26,20 @@ from llama_stack.providers.inline.agents.meta_reference.config import MetaRefere
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from llama_stack.providers.inline.agents.meta_reference.persistence import AgentInfo
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@pytest.fixture(autouse=True)
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def setup_backends(tmp_path):
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"""Register KV and SQL store backends for testing."""
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from llama_stack.core.storage.datatypes import SqliteKVStoreConfig, SqliteSqlStoreConfig
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from llama_stack.providers.utils.kvstore.kvstore import register_kvstore_backends
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from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
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kv_path = str(tmp_path / "test_kv.db")
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sql_path = str(tmp_path / "test_sql.db")
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register_kvstore_backends({"kv_default": SqliteKVStoreConfig(db_path=kv_path)})
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register_sqlstore_backends({"sql_default": SqliteSqlStoreConfig(db_path=sql_path)})
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@pytest.fixture
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def mock_apis():
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return {
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@ -40,15 +54,20 @@ def mock_apis():
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@pytest.fixture
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def config(tmp_path):
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from llama_stack.core.storage.datatypes import KVStoreReference, ResponsesStoreReference
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from llama_stack.providers.inline.agents.meta_reference.config import AgentPersistenceConfig
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return MetaReferenceAgentsImplConfig(
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persistence_store={
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"type": "sqlite",
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"db_path": str(tmp_path / "test.db"),
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},
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responses_store={
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"type": "sqlite",
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"db_path": str(tmp_path / "test.db"),
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},
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persistence=AgentPersistenceConfig(
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agent_state=KVStoreReference(
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backend="kv_default",
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namespace="agents",
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),
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responses=ResponsesStoreReference(
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backend="sql_default",
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table_name="responses",
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),
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)
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)
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@ -42,7 +42,7 @@ from llama_stack.apis.inference import (
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)
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from llama_stack.apis.tools.tools import ListToolDefsResponse, ToolDef, ToolGroups, ToolInvocationResult, ToolRuntime
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from llama_stack.core.access_control.access_control import default_policy
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from llama_stack.core.datatypes import ResponsesStoreConfig
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from llama_stack.core.storage.datatypes import ResponsesStoreReference, SqliteSqlStoreConfig
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from llama_stack.providers.inline.agents.meta_reference.responses.openai_responses import (
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OpenAIResponsesImpl,
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)
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@ -50,7 +50,7 @@ from llama_stack.providers.utils.responses.responses_store import (
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ResponsesStore,
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_OpenAIResponseObjectWithInputAndMessages,
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)
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from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
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from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
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from tests.unit.providers.agents.meta_reference.fixtures import load_chat_completion_fixture
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@ -814,6 +814,69 @@ async def test_create_openai_response_with_instructions_and_previous_response(
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assert sent_messages[3].content == "Which is the largest?"
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async def test_create_openai_response_with_previous_response_instructions(
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openai_responses_impl, mock_responses_store, mock_inference_api
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):
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"""Test prepending instructions and previous response with instructions."""
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input_item_message = OpenAIResponseMessage(
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id="123",
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content="Name some towns in Ireland",
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role="user",
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)
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response_output_message = OpenAIResponseMessage(
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id="123",
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content="Galway, Longford, Sligo",
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status="completed",
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role="assistant",
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)
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response = _OpenAIResponseObjectWithInputAndMessages(
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created_at=1,
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id="resp_123",
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model="fake_model",
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output=[response_output_message],
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status="completed",
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text=OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")),
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input=[input_item_message],
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messages=[
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OpenAIUserMessageParam(content="Name some towns in Ireland"),
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OpenAIAssistantMessageParam(content="Galway, Longford, Sligo"),
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],
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instructions="You are a helpful assistant.",
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)
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mock_responses_store.get_response_object.return_value = response
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model = "meta-llama/Llama-3.1-8B-Instruct"
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instructions = "You are a geography expert. Provide concise answers."
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mock_inference_api.openai_chat_completion.return_value = fake_stream()
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# Execute
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await openai_responses_impl.create_openai_response(
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input="Which is the largest?", model=model, instructions=instructions, previous_response_id="123"
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)
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# Verify
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mock_inference_api.openai_chat_completion.assert_called_once()
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call_args = mock_inference_api.openai_chat_completion.call_args
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params = call_args.args[0]
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sent_messages = params.messages
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# Check that instructions were prepended as a system message
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# and that the previous response instructions were not carried over
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assert len(sent_messages) == 4, sent_messages
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assert sent_messages[0].role == "system"
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assert sent_messages[0].content == instructions
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# Check the rest of the messages were converted correctly
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assert sent_messages[1].role == "user"
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assert sent_messages[1].content == "Name some towns in Ireland"
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assert sent_messages[2].role == "assistant"
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assert sent_messages[2].content == "Galway, Longford, Sligo"
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assert sent_messages[3].role == "user"
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assert sent_messages[3].content == "Which is the largest?"
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async def test_list_openai_response_input_items_delegation(openai_responses_impl, mock_responses_store):
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"""Test that list_openai_response_input_items properly delegates to responses_store with correct parameters."""
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# Setup
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@ -854,8 +917,10 @@ async def test_responses_store_list_input_items_logic():
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# Create mock store and response store
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mock_sql_store = AsyncMock()
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backend_name = "sql_responses_test"
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register_sqlstore_backends({backend_name: SqliteSqlStoreConfig(db_path="mock_db_path")})
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responses_store = ResponsesStore(
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ResponsesStoreConfig(sql_store_config=SqliteSqlStoreConfig(db_path="mock_db_path")), policy=default_policy()
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ResponsesStoreReference(backend=backend_name, table_name="responses"), policy=default_policy()
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)
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responses_store.sql_store = mock_sql_store
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@ -12,10 +12,10 @@ from unittest.mock import AsyncMock
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import pytest
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from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreConfig
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from llama_stack.providers.inline.batches.reference.batches import ReferenceBatchesImpl
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from llama_stack.providers.inline.batches.reference.config import ReferenceBatchesImplConfig
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from llama_stack.providers.utils.kvstore import kvstore_impl
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from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
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from llama_stack.providers.utils.kvstore import kvstore_impl, register_kvstore_backends
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@pytest.fixture
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@ -23,8 +23,10 @@ async def provider():
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"""Create a test provider instance with temporary database."""
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with tempfile.TemporaryDirectory() as tmpdir:
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db_path = Path(tmpdir) / "test_batches.db"
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backend_name = "kv_batches_test"
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kvstore_config = SqliteKVStoreConfig(db_path=str(db_path))
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config = ReferenceBatchesImplConfig(kvstore=kvstore_config)
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register_kvstore_backends({backend_name: kvstore_config})
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config = ReferenceBatchesImplConfig(kvstore=KVStoreReference(backend=backend_name, namespace="batches"))
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# Create kvstore and mock APIs
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kvstore = await kvstore_impl(config.kvstore)
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@ -8,8 +8,9 @@ import boto3
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import pytest
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from moto import mock_aws
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from llama_stack.core.storage.datatypes import SqliteSqlStoreConfig, SqlStoreReference
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from llama_stack.providers.remote.files.s3 import S3FilesImplConfig, get_adapter_impl
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from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
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from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
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class MockUploadFile:
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@ -38,11 +39,13 @@ def sample_text_file2():
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def s3_config(tmp_path):
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db_path = tmp_path / "s3_files_metadata.db"
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backend_name = f"sql_s3_{tmp_path.name}"
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register_sqlstore_backends({backend_name: SqliteSqlStoreConfig(db_path=db_path.as_posix())})
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return S3FilesImplConfig(
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bucket_name=f"test-bucket-{tmp_path.name}",
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region="not-a-region",
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auto_create_bucket=True,
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metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix()),
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metadata_store=SqlStoreReference(backend=backend_name, table_name="s3_files_metadata"),
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)
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|
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@ -38,6 +38,28 @@ class OpenAIMixinWithEmbeddingsImpl(OpenAIMixinImpl):
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}
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class OpenAIMixinWithCustomModelConstruction(OpenAIMixinImpl):
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"""Test implementation that uses construct_model_from_identifier to add rerank models"""
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embedding_model_metadata: dict[str, dict[str, int]] = {
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"text-embedding-3-small": {"embedding_dimension": 1536, "context_length": 8192},
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"text-embedding-ada-002": {"embedding_dimension": 1536, "context_length": 8192},
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}
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# Adds rerank models via construct_model_from_identifier
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rerank_model_ids: set[str] = {"rerank-model-1", "rerank-model-2"}
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def construct_model_from_identifier(self, identifier: str) -> Model:
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if identifier in self.rerank_model_ids:
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return Model(
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provider_id=self.__provider_id__, # type: ignore[attr-defined]
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provider_resource_id=identifier,
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identifier=identifier,
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model_type=ModelType.rerank,
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)
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return super().construct_model_from_identifier(identifier)
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@pytest.fixture
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def mixin():
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"""Create a test instance of OpenAIMixin with mocked model_store"""
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@ -62,6 +84,13 @@ def mixin_with_embeddings():
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return OpenAIMixinWithEmbeddingsImpl(config=config)
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@pytest.fixture
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def mixin_with_custom_model_construction():
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"""Create a test instance using custom construct_model_from_identifier"""
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config = RemoteInferenceProviderConfig()
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return OpenAIMixinWithCustomModelConstruction(config=config)
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@pytest.fixture
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def mock_models():
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"""Create multiple mock OpenAI model objects"""
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@ -113,6 +142,19 @@ def mock_client_context():
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return _mock_client_context
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def _assert_models_match_expected(actual_models, expected_models):
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"""Verify the models match expected attributes.
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Args:
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actual_models: List of models to verify
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expected_models: Mapping of model identifier to expected attribute values
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"""
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for identifier, expected_attrs in expected_models.items():
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model = next(m for m in actual_models if m.identifier == identifier)
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for attr_name, expected_value in expected_attrs.items():
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assert getattr(model, attr_name) == expected_value
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class TestOpenAIMixinListModels:
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"""Test cases for the list_models method"""
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@ -342,21 +384,71 @@ class TestOpenAIMixinEmbeddingModelMetadata:
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assert result is not None
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assert len(result) == 2
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# Find the models in the result
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embedding_model = next(m for m in result if m.identifier == "text-embedding-3-small")
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llm_model = next(m for m in result if m.identifier == "gpt-4")
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expected_models = {
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"text-embedding-3-small": {
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"model_type": ModelType.embedding,
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"metadata": {"embedding_dimension": 1536, "context_length": 8192},
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"provider_id": "test-provider",
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"provider_resource_id": "text-embedding-3-small",
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},
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"gpt-4": {
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"model_type": ModelType.llm,
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"metadata": {},
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"provider_id": "test-provider",
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"provider_resource_id": "gpt-4",
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},
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}
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# Check embedding model
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assert embedding_model.model_type == ModelType.embedding
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assert embedding_model.metadata == {"embedding_dimension": 1536, "context_length": 8192}
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assert embedding_model.provider_id == "test-provider"
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assert embedding_model.provider_resource_id == "text-embedding-3-small"
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_assert_models_match_expected(result, expected_models)
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# Check LLM model
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assert llm_model.model_type == ModelType.llm
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assert llm_model.metadata == {} # No metadata for LLMs
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assert llm_model.provider_id == "test-provider"
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assert llm_model.provider_resource_id == "gpt-4"
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class TestOpenAIMixinCustomModelConstruction:
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"""Test cases for mixed model types (LLM, embedding, rerank) through construct_model_from_identifier"""
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async def test_mixed_model_types_identification(self, mixin_with_custom_model_construction, mock_client_context):
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"""Test that LLM, embedding, and rerank models are correctly identified with proper types and metadata"""
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# Create mock models: 1 embedding, 1 rerank, 1 LLM
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mock_embedding_model = MagicMock(id="text-embedding-3-small")
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mock_rerank_model = MagicMock(id="rerank-model-1")
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mock_llm_model = MagicMock(id="gpt-4")
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mock_models = [mock_embedding_model, mock_rerank_model, mock_llm_model]
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mock_client = MagicMock()
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async def mock_models_list():
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for model in mock_models:
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yield model
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mock_client.models.list.return_value = mock_models_list()
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with mock_client_context(mixin_with_custom_model_construction, mock_client):
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result = await mixin_with_custom_model_construction.list_models()
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assert result is not None
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assert len(result) == 3
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expected_models = {
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"text-embedding-3-small": {
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"model_type": ModelType.embedding,
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"metadata": {"embedding_dimension": 1536, "context_length": 8192},
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"provider_id": "test-provider",
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"provider_resource_id": "text-embedding-3-small",
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},
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"rerank-model-1": {
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"model_type": ModelType.rerank,
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"metadata": {},
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"provider_id": "test-provider",
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"provider_resource_id": "rerank-model-1",
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},
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"gpt-4": {
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"model_type": ModelType.llm,
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"metadata": {},
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"provider_id": "test-provider",
|
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"provider_resource_id": "gpt-4",
|
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},
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}
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_assert_models_match_expected(result, expected_models)
|
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|
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|
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class TestOpenAIMixinAllowedModels:
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|
|
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|
|
@ -10,15 +10,16 @@ from unittest.mock import AsyncMock, MagicMock, patch
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import numpy as np
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import pytest
|
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|
||||
from llama_stack.apis.vector_dbs import VectorDB
|
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from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
|
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from llama_stack.apis.vector_stores import VectorStore
|
||||
from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreConfig
|
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
|
||||
from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter
|
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from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConfig
|
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from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter
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from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
|
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from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter
|
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from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
|
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from llama_stack.providers.utils.kvstore import register_kvstore_backends
|
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|
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EMBEDDING_DIMENSION = 768
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COLLECTION_PREFIX = "test_collection"
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|
|
@ -30,7 +31,7 @@ def vector_provider(request):
|
|||
|
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|
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@pytest.fixture
|
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def vector_db_id() -> str:
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def vector_store_id() -> str:
|
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return f"test-vector-db-{random.randint(1, 100)}"
|
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|
||||
|
||||
|
|
@ -112,8 +113,9 @@ async def unique_kvstore_config(tmp_path_factory):
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unique_id = f"test_kv_{np.random.randint(1e6)}"
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temp_dir = tmp_path_factory.getbasetemp()
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db_path = str(temp_dir / f"{unique_id}.db")
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return SqliteKVStoreConfig(db_path=db_path)
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backend_name = f"kv_vector_{unique_id}"
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register_kvstore_backends({backend_name: SqliteKVStoreConfig(db_path=db_path)})
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return KVStoreReference(backend=backend_name, namespace=f"vector_io::{unique_id}")
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|
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|
||||
@pytest.fixture(scope="session")
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|
|
@ -138,18 +140,17 @@ async def sqlite_vec_vec_index(embedding_dimension, tmp_path_factory):
|
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async def sqlite_vec_adapter(sqlite_vec_db_path, unique_kvstore_config, mock_inference_api, embedding_dimension):
|
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config = SQLiteVectorIOConfig(
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db_path=sqlite_vec_db_path,
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kvstore=unique_kvstore_config,
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persistence=unique_kvstore_config,
|
||||
)
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adapter = SQLiteVecVectorIOAdapter(
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config=config,
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inference_api=mock_inference_api,
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||||
files_api=None,
|
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models_api=None,
|
||||
)
|
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collection_id = f"sqlite_test_collection_{np.random.randint(1e6)}"
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await adapter.initialize()
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||||
await adapter.register_vector_db(
|
||||
VectorDB(
|
||||
await adapter.register_vector_store(
|
||||
VectorStore(
|
||||
identifier=collection_id,
|
||||
provider_id="test_provider",
|
||||
embedding_model="test_model",
|
||||
|
|
@ -177,17 +178,16 @@ async def faiss_vec_index(embedding_dimension):
|
|||
@pytest.fixture
|
||||
async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding_dimension):
|
||||
config = FaissVectorIOConfig(
|
||||
kvstore=unique_kvstore_config,
|
||||
persistence=unique_kvstore_config,
|
||||
)
|
||||
adapter = FaissVectorIOAdapter(
|
||||
config=config,
|
||||
inference_api=mock_inference_api,
|
||||
files_api=None,
|
||||
models_api=None,
|
||||
)
|
||||
await adapter.initialize()
|
||||
await adapter.register_vector_db(
|
||||
VectorDB(
|
||||
await adapter.register_vector_store(
|
||||
VectorStore(
|
||||
identifier=f"faiss_test_collection_{np.random.randint(1e6)}",
|
||||
provider_id="test_provider",
|
||||
embedding_model="test_model",
|
||||
|
|
@ -215,7 +215,7 @@ def mock_psycopg2_connection():
|
|||
async def pgvector_vec_index(embedding_dimension, mock_psycopg2_connection):
|
||||
connection, cursor = mock_psycopg2_connection
|
||||
|
||||
vector_db = VectorDB(
|
||||
vector_store = VectorStore(
|
||||
identifier="test-vector-db",
|
||||
embedding_model="test-model",
|
||||
embedding_dimension=embedding_dimension,
|
||||
|
|
@ -225,7 +225,7 @@ async def pgvector_vec_index(embedding_dimension, mock_psycopg2_connection):
|
|||
|
||||
with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"):
|
||||
with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.execute_values"):
|
||||
index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE")
|
||||
index = PGVectorIndex(vector_store, embedding_dimension, connection, distance_metric="COSINE")
|
||||
index._test_chunks = []
|
||||
original_add_chunks = index.add_chunks
|
||||
|
||||
|
|
@ -253,7 +253,7 @@ async def pgvector_vec_adapter(unique_kvstore_config, mock_inference_api, embedd
|
|||
db="test_db",
|
||||
user="test_user",
|
||||
password="test_password",
|
||||
kvstore=unique_kvstore_config,
|
||||
persistence=unique_kvstore_config,
|
||||
)
|
||||
|
||||
adapter = PGVectorVectorIOAdapter(config, mock_inference_api, None)
|
||||
|
|
@ -281,30 +281,30 @@ async def pgvector_vec_adapter(unique_kvstore_config, mock_inference_api, embedd
|
|||
await adapter.initialize()
|
||||
adapter.conn = mock_conn
|
||||
|
||||
async def mock_insert_chunks(vector_db_id, chunks, ttl_seconds=None):
|
||||
index = await adapter._get_and_cache_vector_db_index(vector_db_id)
|
||||
async def mock_insert_chunks(vector_store_id, chunks, ttl_seconds=None):
|
||||
index = await adapter._get_and_cache_vector_store_index(vector_store_id)
|
||||
if not index:
|
||||
raise ValueError(f"Vector DB {vector_db_id} not found")
|
||||
raise ValueError(f"Vector DB {vector_store_id} not found")
|
||||
await index.insert_chunks(chunks)
|
||||
|
||||
adapter.insert_chunks = mock_insert_chunks
|
||||
|
||||
async def mock_query_chunks(vector_db_id, query, params=None):
|
||||
index = await adapter._get_and_cache_vector_db_index(vector_db_id)
|
||||
async def mock_query_chunks(vector_store_id, query, params=None):
|
||||
index = await adapter._get_and_cache_vector_store_index(vector_store_id)
|
||||
if not index:
|
||||
raise ValueError(f"Vector DB {vector_db_id} not found")
|
||||
raise ValueError(f"Vector DB {vector_store_id} not found")
|
||||
return await index.query_chunks(query, params)
|
||||
|
||||
adapter.query_chunks = mock_query_chunks
|
||||
|
||||
test_vector_db = VectorDB(
|
||||
test_vector_store = VectorStore(
|
||||
identifier=f"pgvector_test_collection_{random.randint(1, 1_000_000)}",
|
||||
provider_id="test_provider",
|
||||
embedding_model="test_model",
|
||||
embedding_dimension=embedding_dimension,
|
||||
)
|
||||
await adapter.register_vector_db(test_vector_db)
|
||||
adapter.test_collection_id = test_vector_db.identifier
|
||||
await adapter.register_vector_store(test_vector_store)
|
||||
adapter.test_collection_id = test_vector_store.identifier
|
||||
|
||||
yield adapter
|
||||
await adapter.shutdown()
|
||||
|
|
|
|||
|
|
@ -11,9 +11,8 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from llama_stack.apis.files import Files
|
||||
from llama_stack.apis.models import Models
|
||||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
|
||||
from llama_stack.apis.vector_stores import VectorStore
|
||||
from llama_stack.providers.datatypes import HealthStatus
|
||||
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
|
||||
from llama_stack.providers.inline.vector_io.faiss.faiss import (
|
||||
|
|
@ -44,8 +43,8 @@ def embedding_dimension():
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def vector_db_id():
|
||||
return "test_vector_db"
|
||||
def vector_store_id():
|
||||
return "test_vector_store"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -62,12 +61,12 @@ def sample_embeddings(embedding_dimension):
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_vector_db(vector_db_id, embedding_dimension) -> MagicMock:
|
||||
mock_vector_db = MagicMock(spec=VectorDB)
|
||||
mock_vector_db.embedding_model = "mock_embedding_model"
|
||||
mock_vector_db.identifier = vector_db_id
|
||||
mock_vector_db.embedding_dimension = embedding_dimension
|
||||
return mock_vector_db
|
||||
def mock_vector_store(vector_store_id, embedding_dimension) -> MagicMock:
|
||||
mock_vector_store = MagicMock(spec=VectorStore)
|
||||
mock_vector_store.embedding_model = "mock_embedding_model"
|
||||
mock_vector_store.identifier = vector_store_id
|
||||
mock_vector_store.embedding_dimension = embedding_dimension
|
||||
return mock_vector_store
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -76,12 +75,6 @@ def mock_files_api():
|
|||
return mock_api
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_models_api():
|
||||
mock_api = MagicMock(spec=Models)
|
||||
return mock_api
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def faiss_config():
|
||||
config = MagicMock(spec=FaissVectorIOConfig)
|
||||
|
|
@ -117,7 +110,7 @@ async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_
|
|||
assert response.chunks[1] == sample_chunks[1]
|
||||
|
||||
|
||||
async def test_health_success(mock_models_api):
|
||||
async def test_health_success():
|
||||
"""Test that the health check returns OK status when faiss is working correctly."""
|
||||
# Create a fresh instance of FaissVectorIOAdapter for testing
|
||||
config = MagicMock()
|
||||
|
|
@ -126,9 +119,7 @@ async def test_health_success(mock_models_api):
|
|||
|
||||
with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
|
||||
mock_index_flat.return_value = MagicMock()
|
||||
adapter = FaissVectorIOAdapter(
|
||||
config=config, inference_api=inference_api, models_api=mock_models_api, files_api=files_api
|
||||
)
|
||||
adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
|
||||
|
||||
# Calling the health method directly
|
||||
response = await adapter.health()
|
||||
|
|
@ -142,7 +133,7 @@ async def test_health_success(mock_models_api):
|
|||
mock_index_flat.assert_called_once_with(128) # VECTOR_DIMENSION is 128
|
||||
|
||||
|
||||
async def test_health_failure(mock_models_api):
|
||||
async def test_health_failure():
|
||||
"""Test that the health check returns ERROR status when faiss encounters an error."""
|
||||
# Create a fresh instance of FaissVectorIOAdapter for testing
|
||||
config = MagicMock()
|
||||
|
|
@ -152,9 +143,7 @@ async def test_health_failure(mock_models_api):
|
|||
with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
|
||||
mock_index_flat.side_effect = Exception("Test error")
|
||||
|
||||
adapter = FaissVectorIOAdapter(
|
||||
config=config, inference_api=inference_api, models_api=mock_models_api, files_api=files_api
|
||||
)
|
||||
adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
|
||||
|
||||
# Calling the health method directly
|
||||
response = await adapter.health()
|
||||
|
|
|
|||
|
|
@ -6,14 +6,12 @@
|
|||
|
||||
import json
|
||||
import time
|
||||
from unittest.mock import AsyncMock, Mock, patch
|
||||
from unittest.mock import AsyncMock, patch
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.common.errors import VectorStoreNotFoundError
|
||||
from llama_stack.apis.models import Model, ModelType
|
||||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import (
|
||||
Chunk,
|
||||
OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
|
||||
|
|
@ -22,6 +20,7 @@ from llama_stack.apis.vector_io import (
|
|||
VectorStoreChunkingStrategyAuto,
|
||||
VectorStoreFileObject,
|
||||
)
|
||||
from llama_stack.apis.vector_stores import VectorStore
|
||||
from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import VECTOR_DBS_PREFIX
|
||||
|
||||
# This test is a unit test for the inline VectorIO providers. This should only contain
|
||||
|
|
@ -72,7 +71,7 @@ async def test_chunk_id_conflict(vector_index, sample_chunks, embedding_dimensio
|
|||
|
||||
async def test_initialize_adapter_with_existing_kvstore(vector_io_adapter):
|
||||
key = f"{VECTOR_DBS_PREFIX}db1"
|
||||
dummy = VectorDB(
|
||||
dummy = VectorStore(
|
||||
identifier="foo_db", provider_id="test_provider", embedding_model="test_model", embedding_dimension=128
|
||||
)
|
||||
await vector_io_adapter.kvstore.set(key=key, value=json.dumps(dummy.model_dump()))
|
||||
|
|
@ -82,10 +81,10 @@ async def test_initialize_adapter_with_existing_kvstore(vector_io_adapter):
|
|||
|
||||
async def test_persistence_across_adapter_restarts(vector_io_adapter):
|
||||
await vector_io_adapter.initialize()
|
||||
dummy = VectorDB(
|
||||
dummy = VectorStore(
|
||||
identifier="foo_db", provider_id="test_provider", embedding_model="test_model", embedding_dimension=128
|
||||
)
|
||||
await vector_io_adapter.register_vector_db(dummy)
|
||||
await vector_io_adapter.register_vector_store(dummy)
|
||||
await vector_io_adapter.shutdown()
|
||||
|
||||
await vector_io_adapter.initialize()
|
||||
|
|
@ -93,15 +92,15 @@ async def test_persistence_across_adapter_restarts(vector_io_adapter):
|
|||
await vector_io_adapter.shutdown()
|
||||
|
||||
|
||||
async def test_register_and_unregister_vector_db(vector_io_adapter):
|
||||
async def test_register_and_unregister_vector_store(vector_io_adapter):
|
||||
unique_id = f"foo_db_{np.random.randint(1e6)}"
|
||||
dummy = VectorDB(
|
||||
dummy = VectorStore(
|
||||
identifier=unique_id, provider_id="test_provider", embedding_model="test_model", embedding_dimension=128
|
||||
)
|
||||
|
||||
await vector_io_adapter.register_vector_db(dummy)
|
||||
await vector_io_adapter.register_vector_store(dummy)
|
||||
assert dummy.identifier in vector_io_adapter.cache
|
||||
await vector_io_adapter.unregister_vector_db(dummy.identifier)
|
||||
await vector_io_adapter.unregister_vector_store(dummy.identifier)
|
||||
assert dummy.identifier not in vector_io_adapter.cache
|
||||
|
||||
|
||||
|
|
@ -122,7 +121,7 @@ async def test_insert_chunks_calls_underlying_index(vector_io_adapter):
|
|||
|
||||
|
||||
async def test_insert_chunks_missing_db_raises(vector_io_adapter):
|
||||
vector_io_adapter._get_and_cache_vector_db_index = AsyncMock(return_value=None)
|
||||
vector_io_adapter._get_and_cache_vector_store_index = AsyncMock(return_value=None)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
await vector_io_adapter.insert_chunks("db_not_exist", [])
|
||||
|
|
@ -171,7 +170,7 @@ async def test_query_chunks_calls_underlying_index_and_returns(vector_io_adapter
|
|||
|
||||
|
||||
async def test_query_chunks_missing_db_raises(vector_io_adapter):
|
||||
vector_io_adapter._get_and_cache_vector_db_index = AsyncMock(return_value=None)
|
||||
vector_io_adapter._get_and_cache_vector_store_index = AsyncMock(return_value=None)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
await vector_io_adapter.query_chunks("db_missing", "q", None)
|
||||
|
|
@ -183,7 +182,7 @@ async def test_save_openai_vector_store(vector_io_adapter):
|
|||
"id": store_id,
|
||||
"name": "Test Store",
|
||||
"description": "A test OpenAI vector store",
|
||||
"vector_db_id": "test_db",
|
||||
"vector_store_id": "test_db",
|
||||
"embedding_model": "test_model",
|
||||
}
|
||||
|
||||
|
|
@ -199,7 +198,7 @@ async def test_update_openai_vector_store(vector_io_adapter):
|
|||
"id": store_id,
|
||||
"name": "Test Store",
|
||||
"description": "A test OpenAI vector store",
|
||||
"vector_db_id": "test_db",
|
||||
"vector_store_id": "test_db",
|
||||
"embedding_model": "test_model",
|
||||
}
|
||||
|
||||
|
|
@ -215,7 +214,7 @@ async def test_delete_openai_vector_store(vector_io_adapter):
|
|||
"id": store_id,
|
||||
"name": "Test Store",
|
||||
"description": "A test OpenAI vector store",
|
||||
"vector_db_id": "test_db",
|
||||
"vector_store_id": "test_db",
|
||||
"embedding_model": "test_model",
|
||||
}
|
||||
|
||||
|
|
@ -230,7 +229,7 @@ async def test_load_openai_vector_stores(vector_io_adapter):
|
|||
"id": store_id,
|
||||
"name": "Test Store",
|
||||
"description": "A test OpenAI vector store",
|
||||
"vector_db_id": "test_db",
|
||||
"vector_store_id": "test_db",
|
||||
"embedding_model": "test_model",
|
||||
}
|
||||
|
||||
|
|
@ -996,101 +995,11 @@ async def test_max_concurrent_files_per_batch(vector_io_adapter):
|
|||
assert batch.file_counts.in_progress == 8
|
||||
|
||||
|
||||
async def test_get_default_embedding_model_success(vector_io_adapter):
|
||||
"""Test successful default embedding model detection."""
|
||||
# Mock models API with a default model
|
||||
mock_models_api = Mock()
|
||||
mock_models_api.list_models = AsyncMock(
|
||||
return_value=Mock(
|
||||
data=[
|
||||
Model(
|
||||
identifier="nomic-embed-text-v1.5",
|
||||
model_type=ModelType.embedding,
|
||||
provider_id="test-provider",
|
||||
metadata={
|
||||
"embedding_dimension": 768,
|
||||
"default_configured": True,
|
||||
},
|
||||
)
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
vector_io_adapter.models_api = mock_models_api
|
||||
result = await vector_io_adapter._get_default_embedding_model_and_dimension()
|
||||
|
||||
assert result is not None
|
||||
model_id, dimension = result
|
||||
assert model_id == "nomic-embed-text-v1.5"
|
||||
assert dimension == 768
|
||||
|
||||
|
||||
async def test_get_default_embedding_model_multiple_defaults_error(vector_io_adapter):
|
||||
"""Test error when multiple models are marked as default."""
|
||||
mock_models_api = Mock()
|
||||
mock_models_api.list_models = AsyncMock(
|
||||
return_value=Mock(
|
||||
data=[
|
||||
Model(
|
||||
identifier="model1",
|
||||
model_type=ModelType.embedding,
|
||||
provider_id="test-provider",
|
||||
metadata={"embedding_dimension": 768, "default_configured": True},
|
||||
),
|
||||
Model(
|
||||
identifier="model2",
|
||||
model_type=ModelType.embedding,
|
||||
provider_id="test-provider",
|
||||
metadata={"embedding_dimension": 512, "default_configured": True},
|
||||
),
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
vector_io_adapter.models_api = mock_models_api
|
||||
|
||||
with pytest.raises(ValueError, match="Multiple embedding models marked as default_configured=True"):
|
||||
await vector_io_adapter._get_default_embedding_model_and_dimension()
|
||||
|
||||
|
||||
async def test_openai_create_vector_store_uses_default_model(vector_io_adapter):
|
||||
"""Test that vector store creation uses default embedding model when none specified."""
|
||||
# Mock models API and dependencies
|
||||
mock_models_api = Mock()
|
||||
mock_models_api.list_models = AsyncMock(
|
||||
return_value=Mock(
|
||||
data=[
|
||||
Model(
|
||||
identifier="default-model",
|
||||
model_type=ModelType.embedding,
|
||||
provider_id="test-provider",
|
||||
metadata={"embedding_dimension": 512, "default_configured": True},
|
||||
)
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
vector_io_adapter.models_api = mock_models_api
|
||||
vector_io_adapter.register_vector_db = AsyncMock()
|
||||
vector_io_adapter.__provider_id__ = "test-provider"
|
||||
|
||||
# Create vector store without specifying embedding model
|
||||
params = OpenAICreateVectorStoreRequestWithExtraBody(name="test-store")
|
||||
result = await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
||||
# Verify the vector store was created with default model
|
||||
assert result.name == "test-store"
|
||||
vector_io_adapter.register_vector_db.assert_called_once()
|
||||
call_args = vector_io_adapter.register_vector_db.call_args[0][0]
|
||||
assert call_args.embedding_model == "default-model"
|
||||
assert call_args.embedding_dimension == 512
|
||||
|
||||
|
||||
async def test_embedding_config_from_metadata(vector_io_adapter):
|
||||
"""Test that embedding configuration is correctly extracted from metadata."""
|
||||
|
||||
# Mock register_vector_db to avoid actual registration
|
||||
vector_io_adapter.register_vector_db = AsyncMock()
|
||||
# Mock register_vector_store to avoid actual registration
|
||||
vector_io_adapter.register_vector_store = AsyncMock()
|
||||
# Set provider_id attribute for the adapter
|
||||
vector_io_adapter.__provider_id__ = "test_provider"
|
||||
|
||||
|
|
@ -1106,9 +1015,9 @@ async def test_embedding_config_from_metadata(vector_io_adapter):
|
|||
|
||||
await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
||||
# Verify VectorDB was registered with correct embedding config from metadata
|
||||
vector_io_adapter.register_vector_db.assert_called_once()
|
||||
call_args = vector_io_adapter.register_vector_db.call_args[0][0]
|
||||
# Verify VectorStore was registered with correct embedding config from metadata
|
||||
vector_io_adapter.register_vector_store.assert_called_once()
|
||||
call_args = vector_io_adapter.register_vector_store.call_args[0][0]
|
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assert call_args.embedding_model == "test-embedding-model"
|
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assert call_args.embedding_dimension == 512
|
||||
|
||||
|
|
@ -1116,8 +1025,8 @@ async def test_embedding_config_from_metadata(vector_io_adapter):
|
|||
async def test_embedding_config_from_extra_body(vector_io_adapter):
|
||||
"""Test that embedding configuration is correctly extracted from extra_body when metadata is empty."""
|
||||
|
||||
# Mock register_vector_db to avoid actual registration
|
||||
vector_io_adapter.register_vector_db = AsyncMock()
|
||||
# Mock register_vector_store to avoid actual registration
|
||||
vector_io_adapter.register_vector_store = AsyncMock()
|
||||
# Set provider_id attribute for the adapter
|
||||
vector_io_adapter.__provider_id__ = "test_provider"
|
||||
|
||||
|
|
@ -1133,9 +1042,9 @@ async def test_embedding_config_from_extra_body(vector_io_adapter):
|
|||
|
||||
await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
||||
# Verify VectorDB was registered with correct embedding config from extra_body
|
||||
vector_io_adapter.register_vector_db.assert_called_once()
|
||||
call_args = vector_io_adapter.register_vector_db.call_args[0][0]
|
||||
# Verify VectorStore was registered with correct embedding config from extra_body
|
||||
vector_io_adapter.register_vector_store.assert_called_once()
|
||||
call_args = vector_io_adapter.register_vector_store.call_args[0][0]
|
||||
assert call_args.embedding_model == "extra-body-model"
|
||||
assert call_args.embedding_dimension == 1024
|
||||
|
||||
|
|
@ -1143,8 +1052,8 @@ async def test_embedding_config_from_extra_body(vector_io_adapter):
|
|||
async def test_embedding_config_consistency_check_passes(vector_io_adapter):
|
||||
"""Test that consistent embedding config in both metadata and extra_body passes validation."""
|
||||
|
||||
# Mock register_vector_db to avoid actual registration
|
||||
vector_io_adapter.register_vector_db = AsyncMock()
|
||||
# Mock register_vector_store to avoid actual registration
|
||||
vector_io_adapter.register_vector_store = AsyncMock()
|
||||
# Set provider_id attribute for the adapter
|
||||
vector_io_adapter.__provider_id__ = "test_provider"
|
||||
|
||||
|
|
@ -1164,61 +1073,17 @@ async def test_embedding_config_consistency_check_passes(vector_io_adapter):
|
|||
await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
||||
# Should not raise any error and use metadata config
|
||||
vector_io_adapter.register_vector_db.assert_called_once()
|
||||
call_args = vector_io_adapter.register_vector_db.call_args[0][0]
|
||||
vector_io_adapter.register_vector_store.assert_called_once()
|
||||
call_args = vector_io_adapter.register_vector_store.call_args[0][0]
|
||||
assert call_args.embedding_model == "consistent-model"
|
||||
assert call_args.embedding_dimension == 768
|
||||
|
||||
|
||||
async def test_embedding_config_inconsistency_errors(vector_io_adapter):
|
||||
"""Test that inconsistent embedding config between metadata and extra_body raises errors."""
|
||||
|
||||
# Mock register_vector_db to avoid actual registration
|
||||
vector_io_adapter.register_vector_db = AsyncMock()
|
||||
# Set provider_id attribute for the adapter
|
||||
vector_io_adapter.__provider_id__ = "test_provider"
|
||||
|
||||
# Test with inconsistent embedding model
|
||||
params = OpenAICreateVectorStoreRequestWithExtraBody(
|
||||
name="test_store",
|
||||
metadata={
|
||||
"embedding_model": "metadata-model",
|
||||
"embedding_dimension": "768",
|
||||
},
|
||||
**{
|
||||
"embedding_model": "extra-body-model",
|
||||
"embedding_dimension": 768,
|
||||
},
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="Embedding model inconsistent between metadata"):
|
||||
await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
||||
# Reset mock for second test
|
||||
vector_io_adapter.register_vector_db.reset_mock()
|
||||
|
||||
# Test with inconsistent embedding dimension
|
||||
params = OpenAICreateVectorStoreRequestWithExtraBody(
|
||||
name="test_store",
|
||||
metadata={
|
||||
"embedding_model": "same-model",
|
||||
"embedding_dimension": "512",
|
||||
},
|
||||
**{
|
||||
"embedding_model": "same-model",
|
||||
"embedding_dimension": 1024,
|
||||
},
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="Embedding dimension inconsistent between metadata"):
|
||||
await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
||||
|
||||
async def test_embedding_config_defaults_when_missing(vector_io_adapter):
|
||||
"""Test that embedding dimension defaults to 768 when not provided."""
|
||||
|
||||
# Mock register_vector_db to avoid actual registration
|
||||
vector_io_adapter.register_vector_db = AsyncMock()
|
||||
# Mock register_vector_store to avoid actual registration
|
||||
vector_io_adapter.register_vector_store = AsyncMock()
|
||||
# Set provider_id attribute for the adapter
|
||||
vector_io_adapter.__provider_id__ = "test_provider"
|
||||
|
||||
|
|
@ -1234,8 +1099,8 @@ async def test_embedding_config_defaults_when_missing(vector_io_adapter):
|
|||
await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
||||
# Should default to 768 dimensions
|
||||
vector_io_adapter.register_vector_db.assert_called_once()
|
||||
call_args = vector_io_adapter.register_vector_db.call_args[0][0]
|
||||
vector_io_adapter.register_vector_store.assert_called_once()
|
||||
call_args = vector_io_adapter.register_vector_store.call_args[0][0]
|
||||
assert call_args.embedding_model == "model-without-dimension"
|
||||
assert call_args.embedding_dimension == 768
|
||||
|
||||
|
|
@ -1243,8 +1108,8 @@ async def test_embedding_config_defaults_when_missing(vector_io_adapter):
|
|||
async def test_embedding_config_required_model_missing(vector_io_adapter):
|
||||
"""Test that missing embedding model raises error."""
|
||||
|
||||
# Mock register_vector_db to avoid actual registration
|
||||
vector_io_adapter.register_vector_db = AsyncMock()
|
||||
# Mock register_vector_store to avoid actual registration
|
||||
vector_io_adapter.register_vector_store = AsyncMock()
|
||||
# Set provider_id attribute for the adapter
|
||||
vector_io_adapter.__provider_id__ = "test_provider"
|
||||
# Mock the default model lookup to return None (no default model available)
|
||||
|
|
@ -1253,5 +1118,5 @@ async def test_embedding_config_required_model_missing(vector_io_adapter):
|
|||
# Test with no embedding model provided
|
||||
params = OpenAICreateVectorStoreRequestWithExtraBody(name="test_store", metadata={})
|
||||
|
||||
with pytest.raises(ValueError, match="embedding_model is required in extra_body when creating a vector store"):
|
||||
with pytest.raises(ValueError, match="embedding_model is required"):
|
||||
await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue