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chore: make OpenAIMixin maintainable, turn OpenAIMixin into a pydantic.BaseModel
- implement get_api_key instead of relying on LiteLLMOpenAIMixin.get_api_key - remove use of LiteLLMOpenAIMixin - add default initialize/shutdown methods to OpenAIMixin - remove __init__s to allow proper pydantic construction - remove dead code from vllm adapter and associated / duplicate unit tests - update vllm adapter to use openaimixin for model registration - remove ModelRegistryHelper from fireworks & together adapters - remove Inference from nvidia adapter - complete type hints on embedding_model_metadata - allow extra fields on OpenAIMixin, for model_store, __provider_id__, etc - new recordings for ollama - enhance the list models error handling w/ new tests - update cerebras (remove cerebras-cloud-sdk) and anthropic (custom model listing) inference adapters - parametrized test_inference_client_caching - remove cerebras, databricks, fireworks, together from blanket mypy exclude
This commit is contained in:
Matthew Farrellee
parent
351c4b98e4
commit
fd06717d87
64 changed files with 12901 additions and 1734 deletions
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@ -5,6 +5,7 @@
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# the root directory of this source tree.
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import json
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from collections.abc import Iterable
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from unittest.mock import AsyncMock, MagicMock, Mock, PropertyMock, patch
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import pytest
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@ -13,6 +14,7 @@ from pydantic import BaseModel, Field
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from llama_stack.apis.inference import Model, OpenAIUserMessageParam
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from llama_stack.apis.models import ModelType
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from llama_stack.core.request_headers import request_provider_data_context
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from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
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from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
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@ -29,7 +31,7 @@ class OpenAIMixinImpl(OpenAIMixin):
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class OpenAIMixinWithEmbeddingsImpl(OpenAIMixinImpl):
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"""Test implementation with embedding model metadata"""
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embedding_model_metadata = {
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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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@ -38,7 +40,8 @@ class OpenAIMixinWithEmbeddingsImpl(OpenAIMixinImpl):
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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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mixin_instance = OpenAIMixinImpl()
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config = RemoteInferenceProviderConfig()
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mixin_instance = OpenAIMixinImpl(config=config)
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# just enough to satisfy _get_provider_model_id calls
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mock_model_store = MagicMock()
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@ -53,7 +56,8 @@ def mixin():
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@pytest.fixture
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def mixin_with_embeddings():
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"""Create a test instance of OpenAIMixin with embedding model metadata"""
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return OpenAIMixinWithEmbeddingsImpl()
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config = RemoteInferenceProviderConfig()
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return OpenAIMixinWithEmbeddingsImpl(config=config)
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@pytest.fixture
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@ -498,13 +502,296 @@ class OpenAIMixinWithProviderData(OpenAIMixinImpl):
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return "default-base-url"
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class OpenAIMixinWithCustomGetModels(OpenAIMixinImpl):
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"""Test implementation with custom get_models override"""
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def __init__(self, config, custom_model_ids):
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super().__init__(config=config)
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self._custom_model_ids = custom_model_ids
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async def get_models(self) -> Iterable[str] | None:
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"""Return custom model IDs list"""
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return self._custom_model_ids
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class TestOpenAIMixinCustomGetModels:
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"""Test cases for custom get_models() implementation functionality"""
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@pytest.fixture
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def custom_model_ids_list(self):
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"""Create a list of custom model ID strings"""
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return ["custom-model-1", "custom-model-2", "custom-embedding"]
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@pytest.fixture
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def mixin_with_custom_get_models(self, custom_model_ids_list):
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"""Create mixin instance with custom get_models implementation"""
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config = RemoteInferenceProviderConfig()
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mixin = OpenAIMixinWithCustomGetModels(config=config, custom_model_ids=custom_model_ids_list)
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# Add embedding metadata to test that feature still works
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mixin.embedding_model_metadata = {"custom-embedding": {"embedding_dimension": 768, "context_length": 512}}
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return mixin
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async def test_custom_get_models_is_used(self, mixin_with_custom_get_models, custom_model_ids_list):
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"""Test that custom get_models() implementation is used instead of client.models.list()"""
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result = await mixin_with_custom_get_models.list_models()
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assert result is not None
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assert len(result) == 3
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# Verify all custom models are present
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identifiers = {m.identifier for m in result}
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assert "custom-model-1" in identifiers
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assert "custom-model-2" in identifiers
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assert "custom-embedding" in identifiers
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async def test_custom_get_models_populates_cache(self, mixin_with_custom_get_models):
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"""Test that custom get_models() results are cached"""
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assert len(mixin_with_custom_get_models._model_cache) == 0
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await mixin_with_custom_get_models.list_models()
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assert len(mixin_with_custom_get_models._model_cache) == 3
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assert "custom-model-1" in mixin_with_custom_get_models._model_cache
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assert "custom-model-2" in mixin_with_custom_get_models._model_cache
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assert "custom-embedding" in mixin_with_custom_get_models._model_cache
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async def test_custom_get_models_respects_allowed_models(self):
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"""Test that custom get_models() respects allowed_models filtering"""
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config = RemoteInferenceProviderConfig()
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mixin = OpenAIMixinWithCustomGetModels(config=config, custom_model_ids=["model-1", "model-2", "model-3"])
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mixin.allowed_models = ["model-1"]
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result = await mixin.list_models()
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assert result is not None
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assert len(result) == 1
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assert result[0].identifier == "model-1"
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async def test_custom_get_models_with_embedding_metadata(self, mixin_with_custom_get_models):
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"""Test that custom get_models() works with embedding_model_metadata"""
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result = await mixin_with_custom_get_models.list_models()
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# Find the embedding model
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embedding_model = next((m for m in result if m.identifier == "custom-embedding"), None)
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assert embedding_model is not None
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assert embedding_model.model_type == ModelType.embedding
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assert embedding_model.metadata == {"embedding_dimension": 768, "context_length": 512}
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# Verify LLM models
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llm_models = [m for m in result if m.model_type == ModelType.llm]
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assert len(llm_models) == 2
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async def test_custom_get_models_with_empty_list(self, mock_client_with_empty_models, mock_client_context):
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"""Test that custom get_models() handles empty list correctly"""
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config = RemoteInferenceProviderConfig()
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mixin = OpenAIMixinWithCustomGetModels(config=config, custom_model_ids=[])
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# Empty list from get_models() falls back to client.models.list()
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with mock_client_context(mixin, mock_client_with_empty_models):
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result = await mixin.list_models()
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assert result is not None
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assert len(result) == 0
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assert len(mixin._model_cache) == 0
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async def test_default_get_models_returns_none(self, mixin):
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"""Test that default get_models() implementation returns None"""
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custom_models = await mixin.get_models()
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assert custom_models is None
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async def test_fallback_to_client_when_get_models_returns_none(
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self, mixin, mock_client_with_models, mock_client_context
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):
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"""Test that when get_models() returns None, falls back to client.models.list()"""
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# Default get_models() returns None, so should use client
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with mock_client_context(mixin, mock_client_with_models):
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result = await mixin.list_models()
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assert result is not None
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assert len(result) == 3
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mock_client_with_models.models.list.assert_called_once()
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async def test_custom_get_models_creates_proper_model_objects(self):
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"""Test that custom get_models() model IDs are converted to proper Model objects"""
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config = RemoteInferenceProviderConfig()
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model_ids = ["gpt-4", "gpt-3.5-turbo"]
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mixin = OpenAIMixinWithCustomGetModels(config=config, custom_model_ids=model_ids)
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result = await mixin.list_models()
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assert result is not None
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assert len(result) == 2
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for model in result:
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assert isinstance(model, Model)
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assert model.provider_id == "test-provider"
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assert model.identifier in model_ids
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assert model.provider_resource_id in model_ids
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assert model.model_type == ModelType.llm
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async def test_custom_get_models_bypasses_client(self, mock_client_context):
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"""Test that providing get_models() means client.models.list() is NOT called"""
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config = RemoteInferenceProviderConfig()
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mixin = OpenAIMixinWithCustomGetModels(config=config, custom_model_ids=["model-1", "model-2"])
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# Create a mock client that should NOT be called
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mock_client = MagicMock()
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mock_client.models.list = MagicMock(side_effect=AssertionError("client.models.list should not be called!"))
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with mock_client_context(mixin, mock_client):
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result = await mixin.list_models()
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# Should succeed without calling client.models.list
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assert result is not None
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assert len(result) == 2
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mock_client.models.list.assert_not_called()
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async def test_get_models_wrong_type_raises_error(self):
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"""Test that get_models() returning non-string items results in an error"""
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class BadGetModelsAdapter(OpenAIMixinImpl):
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async def get_models(self) -> Iterable[str] | None:
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# Return list with non-string items
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return [["nested", "list"], {"key": "value"}] # type: ignore
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config = RemoteInferenceProviderConfig()
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mixin = BadGetModelsAdapter(config=config)
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# Should raise ValueError for non-string model ID
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with pytest.raises(ValueError, match="Model ID .* from get_models\\(\\) is not a string"):
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await mixin.list_models()
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async def test_get_models_non_iterable_raises_error(self):
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"""Test that get_models() returning non-iterable type raises error"""
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class NonIterableGetModelsAdapter(OpenAIMixinImpl):
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async def get_models(self) -> Iterable[str] | None:
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# Return non-iterable type
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return 42 # type: ignore
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config = RemoteInferenceProviderConfig()
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mixin = NonIterableGetModelsAdapter(config=config)
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# Should raise TypeError when trying to convert to list
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with pytest.raises(TypeError, match="'int' object is not iterable"):
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await mixin.list_models()
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async def test_get_models_with_none_items_raises_error(self):
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"""Test that get_models() returning list with None items causes error"""
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class NoneItemsAdapter(OpenAIMixinImpl):
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async def get_models(self) -> Iterable[str] | None:
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# Return list with None items
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return [None, "valid-model", None] # type: ignore
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config = RemoteInferenceProviderConfig()
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mixin = NoneItemsAdapter(config=config)
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# Should raise ValueError for non-string model ID
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with pytest.raises(ValueError, match="Model ID .* from get_models\\(\\) is not a string"):
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await mixin.list_models()
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async def test_get_models_with_non_string_items_raises_error(self):
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"""Test that get_models() returning non-string items raises ValueError"""
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class NonStringItemsAdapter(OpenAIMixinImpl):
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async def get_models(self) -> Iterable[str] | None:
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# Return list with non-string items (integers)
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return ["valid-model", 123, "another-model"] # type: ignore
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config = RemoteInferenceProviderConfig()
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mixin = NonStringItemsAdapter(config=config)
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# Should raise ValueError for non-string model ID
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with pytest.raises(ValueError, match="Model ID 123 from get_models\\(\\) is not a string"):
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await mixin.list_models()
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async def test_embedding_models_from_custom_get_models_have_correct_type(self, mixin_with_custom_get_models):
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"""Test that embedding models from custom get_models() are properly typed as embedding"""
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result = await mixin_with_custom_get_models.list_models()
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# Verify we have both LLM and embedding models
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llm_models = [m for m in result if m.model_type == ModelType.llm]
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embedding_models = [m for m in result if m.model_type == ModelType.embedding]
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assert len(llm_models) == 2
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assert len(embedding_models) == 1
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assert embedding_models[0].identifier == "custom-embedding"
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async def test_llm_models_from_custom_get_models_have_correct_type(self):
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"""Test that LLM models from custom get_models() are properly typed as llm"""
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config = RemoteInferenceProviderConfig()
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mixin = OpenAIMixinWithCustomGetModels(config=config, custom_model_ids=["gpt-4", "claude-3"])
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result = await mixin.list_models()
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assert result is not None
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assert len(result) == 2
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for model in result:
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assert model.model_type == ModelType.llm
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async def test_get_models_accepts_various_iterables(self):
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"""Test that get_models() accepts tuples, sets, generators, etc."""
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# Test with tuple
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class TupleGetModelsAdapter(OpenAIMixinImpl):
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async def get_models(self) -> Iterable[str] | None:
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return ("model-1", "model-2", "model-3")
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config = RemoteInferenceProviderConfig()
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mixin = TupleGetModelsAdapter(config=config)
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result = await mixin.list_models()
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assert result is not None
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assert len(result) == 3
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# Test with generator
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class GeneratorGetModelsAdapter(OpenAIMixinImpl):
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async def get_models(self) -> Iterable[str] | None:
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def gen():
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yield "gen-model-1"
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yield "gen-model-2"
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return gen()
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mixin = GeneratorGetModelsAdapter(config=config)
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result = await mixin.list_models()
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assert result is not None
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assert len(result) == 2
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# Test with set (order may vary)
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class SetGetModelsAdapter(OpenAIMixinImpl):
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async def get_models(self) -> Iterable[str] | None:
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return {"set-model-1", "set-model-2"}
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mixin = SetGetModelsAdapter(config=config)
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result = await mixin.list_models()
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assert result is not None
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assert len(result) == 2
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async def test_get_models_exception_propagates(self):
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"""Test that when get_models() raises an exception, it propagates to the caller"""
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class FailingGetModelsAdapter(OpenAIMixinImpl):
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async def get_models(self) -> Iterable[str] | None:
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# Simulate an exception during custom model listing
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raise RuntimeError("Failed to fetch custom models")
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config = RemoteInferenceProviderConfig()
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mixin = FailingGetModelsAdapter(config=config)
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# Exception should propagate and not fall back to client.models.list()
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with pytest.raises(RuntimeError, match="Failed to fetch custom models"):
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await mixin.list_models()
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class TestOpenAIMixinProviderDataApiKey:
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"""Test cases for provider_data_api_key_field functionality"""
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@pytest.fixture
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def mixin_with_provider_data_field(self):
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"""Mixin instance with provider_data_api_key_field set"""
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mixin_instance = OpenAIMixinWithProviderData()
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config = RemoteInferenceProviderConfig()
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mixin_instance = OpenAIMixinWithProviderData(config=config)
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# Mock provider_spec for provider data validation
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mock_provider_spec = MagicMock()
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