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

llama_stack/providers/remote/inference/databricks/databricks.py

@@ -9,11 +9,8 @@ from typing import Any
 from databricks.sdk import WorkspaceClient
 
 from llama_stack.apis.inference import (
-    Inference,
-    Model,
     OpenAICompletion,
 )
-from llama_stack.apis.models import ModelType
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
@@ -22,30 +19,31 @@ from .config import DatabricksImplConfig
 logger = get_logger(name=__name__, category="inference::databricks")
 
 
-class DatabricksInferenceAdapter(
-    OpenAIMixin,
-    Inference,
-):
+class DatabricksInferenceAdapter(OpenAIMixin):
+    config: DatabricksImplConfig
+
     # source: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models
-    embedding_model_metadata = {
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "databricks-gte-large-en": {"embedding_dimension": 1024, "context_length": 8192},
         "databricks-bge-large-en": {"embedding_dimension": 1024, "context_length": 512},
     }
 
-    def __init__(self, config: DatabricksImplConfig) -> None:
-        self.config = config
-
     def get_api_key(self) -> str:
         return self.config.api_token.get_secret_value()
 
     def get_base_url(self) -> str:
         return f"{self.config.url}/serving-endpoints"
 
-    async def initialize(self) -> None:
-        return
+    async def get_models(self) -> list[str] | None:
+        return [
+            endpoint.name
+            for endpoint in WorkspaceClient(
+                host=self.config.url, token=self.get_api_key()
+            ).serving_endpoints.list()  # TODO: this is not async
+        ]
 
-    async def shutdown(self) -> None:
-        pass
+    async def should_refresh_models(self) -> bool:
+        return False
 
     async def openai_completion(
         self,
@@ -71,32 +69,3 @@ class DatabricksInferenceAdapter(
         suffix: str | None = None,
     ) -> OpenAICompletion:
         raise NotImplementedError()
-
-    async def list_models(self) -> list[Model] | None:
-        self._model_cache = {}  # from OpenAIMixin
-        ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key())  # TODO: this is not async
-        endpoints = ws_client.serving_endpoints.list()
-        for endpoint in endpoints:
-            model = Model(
-                provider_id=self.__provider_id__,
-                provider_resource_id=endpoint.name,
-                identifier=endpoint.name,
-            )
-            if endpoint.task == "llm/v1/chat":
-                model.model_type = ModelType.llm  # this is redundant, but informative
-            elif endpoint.task == "llm/v1/embeddings":
-                if endpoint.name not in self.embedding_model_metadata:
-                    logger.warning(f"No metadata information available for embedding model {endpoint.name}, skipping.")
-                    continue
-                model.model_type = ModelType.embedding
-                model.metadata = self.embedding_model_metadata[endpoint.name]
-            else:
-                logger.warning(f"Unknown model type, skipping: {endpoint}")
-                continue
-
-            self._model_cache[endpoint.name] = model
-
-        return list(self._model_cache.values())
-
-    async def should_refresh_models(self) -> bool:
-        return False