Merge 309f06829c into a1301911e4

llama_stack/providers/utils/inference/model_registry.py

@@ -35,6 +35,11 @@ class ProviderModelEntry(BaseModel):
     llama_model: str | None = None
     model_type: ModelType = ModelType.llm
     metadata: dict[str, Any] = Field(default_factory=dict)
+    
+    def __init__(self, **data):
+        super().__init__(**data)
+        if self.model_type == ModelType.embedding and "embedding_dimension" not in self.metadata:
+            raise ValueError("Embedding models must specify 'embedding_dimension' in metadata")
 
 
 def get_huggingface_repo(model_descriptor: str) -> str | None:
@@ -103,7 +108,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
                     Model(
                         identifier=id,
                         provider_resource_id=entry.provider_model_id,
-                        model_type=ModelType.llm,
+                        model_type=entry.model_type or ModelType.llm,
                         metadata=entry.metadata,
                         provider_id=self.__provider_id__,
                     )

tests/unit/providers/inference/test_model_registry.py

@@ -0,0 +1,72 @@
+import json
+from unittest.mock import MagicMock
+
+import pytest
+from pydantic import BaseModel, Field
+
+from llama_stack.core.request_headers import request_provider_data_context
+from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry, ModelType
+
+class TestConfig(BaseModel):
+    api_key: str | None = Field(default=None)
+
+class TestProviderDataValidator(BaseModel):
+    test_api_key: str | None = Field(default=None)
+
+MODEL_ENTRIES_WITHOUT_ALIASES = [
+    ProviderModelEntry(model_type=ModelType.llm, provider_model_id="test-llm-model", aliases=[]),
+    ProviderModelEntry(model_type=ModelType.embedding, provider_model_id="test-text-embedding-model", aliases=[], metadata={"embedding_dimension": 1536, "context_length": 8192}),
+]
+
+class TestLiteLLMAdapterWithModelEntries(LiteLLMOpenAIMixin):
+    def __init__(self, config: TestConfig):
+        super().__init__(
+            model_entries=MODEL_ENTRIES_WITHOUT_ALIASES,
+            litellm_provider_name="test",
+            api_key_from_config=config.api_key,
+            provider_data_api_key_field="test_api_key",
+            openai_compat_api_base=None,
+        )
+
+@pytest.fixture
+def adapter_with_model_entries():
+    """Fixture to create adapter with API key in config"""
+    config = TestConfig()
+    adapter = TestLiteLLMAdapterWithModelEntries(config)
+    adapter.__provider_id__ = "test-provider"
+
+    return adapter
+
+async def test_model_types_are_correct(adapter_with_model_entries):
+    """Test that model types are correct"""
+    model_entries = adapter_with_model_entries.model_entries
+    llm_model_entries = [m for m in model_entries if m.model_type == ModelType.llm]
+    assert len(llm_model_entries) == 1
+
+    embedding_model_entries = [m for m in model_entries if m.model_type == ModelType.embedding]
+    assert len(embedding_model_entries) == 1
+
+    models = await adapter_with_model_entries.list_models()
+    llm_models = [m for m in models if m.model_type == ModelType.llm]
+    assert len(llm_models) == len(llm_model_entries)
+
+    embedding_models = [m for m in models if m.model_type == ModelType.embedding]
+    assert len(embedding_models) == len(embedding_model_entries)
+
+def test_embedding_metadata_is_required():
+    with pytest.raises(ValueError):
+        entry1 = ProviderModelEntry(
+            model_type=ModelType.embedding,
+            provider_model_id="test-text-embedding-model",
+            aliases=[],
+            metadata={}
+        )
+    
+    entry2 = ProviderModelEntry(
+        model_type=ModelType.embedding,
+        provider_model_id="test-text-embedding-model",
+        aliases=[],
+        metadata={"embedding_dimension": 1536}
+    )
+    assert entry2.metadata["embedding_dimension"] == 1536