Merge branch 'main' into migrate-vector-store-helpers

llama_stack/cli/utils.py

@@ -6,6 +6,10 @@
 
 import argparse
 
+from llama_stack.log import get_logger
+
+logger = get_logger(name=__name__, category="cli")
+
 
 def add_config_template_args(parser: argparse.ArgumentParser):
     """Add unified config/template arguments with backward compatibility."""
@@ -20,12 +24,25 @@ def add_config_template_args(parser: argparse.ArgumentParser):
     # Backward compatibility arguments (deprecated)
     group.add_argument(
         "--config",
-        dest="config",
+        dest="config_deprecated",
         help="(DEPRECATED) Use positional argument [config] instead. Configuration file path",
     )
 
     group.add_argument(
         "--template",
-        dest="config",
+        dest="template_deprecated",
         help="(DEPRECATED) Use positional argument [config] instead. Template name",
     )
+
+
+def get_config_from_args(args: argparse.Namespace) -> str | None:
+    """Extract config value from parsed arguments, handling both new and deprecated forms."""
+    if args.config is not None:
+        return str(args.config)
+    elif hasattr(args, "config_deprecated") and args.config_deprecated is not None:
+        logger.warning("Using deprecated --config argument. Use positional argument [config] instead.")
+        return str(args.config_deprecated)
+    elif hasattr(args, "template_deprecated") and args.template_deprecated is not None:
+        logger.warning("Using deprecated --template argument. Use positional argument [config] instead.")
+        return str(args.template_deprecated)
+    return None

llama_stack/distribution/build_container.sh

@@ -19,6 +19,9 @@ UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500}
 # mounting is not supported by docker buildx, so we use COPY instead
 USE_COPY_NOT_MOUNT=${USE_COPY_NOT_MOUNT:-}
 
+# Mount command for cache container .cache, can be overridden by the user if needed
+MOUNT_CACHE=${MOUNT_CACHE:-"--mount=type=cache,id=llama-stack-cache,target=/root/.cache"}
+
 # Path to the run.yaml file in the container
 RUN_CONFIG_PATH=/app/run.yaml
 
@@ -125,11 +128,16 @@ RUN pip install uv
 EOF
 fi
 
+# Set the link mode to copy so that uv doesn't attempt to symlink to the cache directory
+add_to_container << EOF
+ENV UV_LINK_MODE=copy
+EOF
+
 # Add pip dependencies first since llama-stack is what will change most often
 # so we can reuse layers.
 if [ -n "$pip_dependencies" ]; then
   add_to_container << EOF
-RUN uv pip install --no-cache $pip_dependencies
+RUN $MOUNT_CACHE uv pip install $pip_dependencies
 EOF
 fi
 
@@ -137,7 +145,7 @@ if [ -n "$special_pip_deps" ]; then
   IFS='#' read -ra parts <<<"$special_pip_deps"
   for part in "${parts[@]}"; do
     add_to_container <<EOF
-RUN uv pip install --no-cache $part
+RUN $MOUNT_CACHE uv pip install $part
 EOF
   done
 fi
@@ -207,7 +215,7 @@ COPY $dir $mount_point
 EOF
   fi
   add_to_container << EOF
-RUN uv pip install --no-cache -e $mount_point
+RUN $MOUNT_CACHE uv pip install -e $mount_point
 EOF
 }
 
@@ -222,10 +230,10 @@ else
   if [ -n "$TEST_PYPI_VERSION" ]; then
     # these packages are damaged in test-pypi, so install them first
     add_to_container << EOF
-RUN uv pip install fastapi libcst
+RUN $MOUNT_CACHE uv pip install fastapi libcst
 EOF
     add_to_container << EOF
-RUN uv pip install --no-cache --extra-index-url https://test.pypi.org/simple/ \
+RUN $MOUNT_CACHE uv pip install --extra-index-url https://test.pypi.org/simple/ \
   --index-strategy unsafe-best-match \
   llama-stack==$TEST_PYPI_VERSION
 
@@ -237,7 +245,7 @@ EOF
       SPEC_VERSION="llama-stack"
     fi
     add_to_container << EOF
-RUN uv pip install --no-cache $SPEC_VERSION
+RUN $MOUNT_CACHE uv pip install $SPEC_VERSION
 EOF
   fi
 fi

llama_stack/distribution/routers/datasets.py

@@ -57,7 +57,8 @@ class DatasetIORouter(DatasetIO):
         logger.debug(
             f"DatasetIORouter.iterrows: {dataset_id}, {start_index=} {limit=}",
         )
-        return await self.routing_table.get_provider_impl(dataset_id).iterrows(
+        provider = await self.routing_table.get_provider_impl(dataset_id)
+        return await provider.iterrows(
             dataset_id=dataset_id,
             start_index=start_index,
             limit=limit,
@@ -65,7 +66,8 @@ class DatasetIORouter(DatasetIO):
 
     async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None:
         logger.debug(f"DatasetIORouter.append_rows: {dataset_id}, {len(rows)} rows")
-        return await self.routing_table.get_provider_impl(dataset_id).append_rows(
+        provider = await self.routing_table.get_provider_impl(dataset_id)
+        return await provider.append_rows(
             dataset_id=dataset_id,
             rows=rows,
         )

llama_stack/distribution/routers/eval_scoring.py

@@ -44,7 +44,8 @@ class ScoringRouter(Scoring):
         logger.debug(f"ScoringRouter.score_batch: {dataset_id}")
         res = {}
         for fn_identifier in scoring_functions.keys():
-            score_response = await self.routing_table.get_provider_impl(fn_identifier).score_batch(
+            provider = await self.routing_table.get_provider_impl(fn_identifier)
+            score_response = await provider.score_batch(
                 dataset_id=dataset_id,
                 scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
             )
@@ -66,7 +67,8 @@ class ScoringRouter(Scoring):
         res = {}
         # look up and map each scoring function to its provider impl
         for fn_identifier in scoring_functions.keys():
-            score_response = await self.routing_table.get_provider_impl(fn_identifier).score(
+            provider = await self.routing_table.get_provider_impl(fn_identifier)
+            score_response = await provider.score(
                 input_rows=input_rows,
                 scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
             )
@@ -97,7 +99,8 @@ class EvalRouter(Eval):
         benchmark_config: BenchmarkConfig,
     ) -> Job:
         logger.debug(f"EvalRouter.run_eval: {benchmark_id}")
-        return await self.routing_table.get_provider_impl(benchmark_id).run_eval(
+        provider = await self.routing_table.get_provider_impl(benchmark_id)
+        return await provider.run_eval(
             benchmark_id=benchmark_id,
             benchmark_config=benchmark_config,
         )
@@ -110,7 +113,8 @@ class EvalRouter(Eval):
         benchmark_config: BenchmarkConfig,
     ) -> EvaluateResponse:
         logger.debug(f"EvalRouter.evaluate_rows: {benchmark_id}, {len(input_rows)} rows")
-        return await self.routing_table.get_provider_impl(benchmark_id).evaluate_rows(
+        provider = await self.routing_table.get_provider_impl(benchmark_id)
+        return await provider.evaluate_rows(
             benchmark_id=benchmark_id,
             input_rows=input_rows,
             scoring_functions=scoring_functions,
@@ -123,7 +127,8 @@ class EvalRouter(Eval):
         job_id: str,
     ) -> Job:
         logger.debug(f"EvalRouter.job_status: {benchmark_id}, {job_id}")
-        return await self.routing_table.get_provider_impl(benchmark_id).job_status(benchmark_id, job_id)
+        provider = await self.routing_table.get_provider_impl(benchmark_id)
+        return await provider.job_status(benchmark_id, job_id)
 
     async def job_cancel(
         self,
@@ -131,7 +136,8 @@ class EvalRouter(Eval):
         job_id: str,
     ) -> None:
         logger.debug(f"EvalRouter.job_cancel: {benchmark_id}, {job_id}")
-        await self.routing_table.get_provider_impl(benchmark_id).job_cancel(
+        provider = await self.routing_table.get_provider_impl(benchmark_id)
+        await provider.job_cancel(
             benchmark_id,
             job_id,
         )
@@ -142,7 +148,8 @@ class EvalRouter(Eval):
         job_id: str,
     ) -> EvaluateResponse:
         logger.debug(f"EvalRouter.job_result: {benchmark_id}, {job_id}")
-        return await self.routing_table.get_provider_impl(benchmark_id).job_result(
+        provider = await self.routing_table.get_provider_impl(benchmark_id)
+        return await provider.job_result(
             benchmark_id,
             job_id,
         )

llama_stack/distribution/routers/inference.py

@@ -231,7 +231,7 @@ class InferenceRouter(Inference):
             logprobs=logprobs,
             tool_config=tool_config,
         )
-        provider = self.routing_table.get_provider_impl(model_id)
+        provider = await self.routing_table.get_provider_impl(model_id)
         prompt_tokens = await self._count_tokens(messages, tool_config.tool_prompt_format)
 
         if stream:
@@ -292,7 +292,7 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.batch_chat_completion: {model_id=}, {len(messages_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}",
         )
-        provider = self.routing_table.get_provider_impl(model_id)
+        provider = await self.routing_table.get_provider_impl(model_id)
         return await provider.batch_chat_completion(
             model_id=model_id,
             messages_batch=messages_batch,
@@ -322,7 +322,7 @@ class InferenceRouter(Inference):
             raise ValueError(f"Model '{model_id}' not found")
         if model.model_type == ModelType.embedding:
             raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
-        provider = self.routing_table.get_provider_impl(model_id)
+        provider = await self.routing_table.get_provider_impl(model_id)
         params = dict(
             model_id=model_id,
             content=content,
@@ -378,7 +378,7 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.batch_completion: {model_id=}, {len(content_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}",
         )
-        provider = self.routing_table.get_provider_impl(model_id)
+        provider = await self.routing_table.get_provider_impl(model_id)
         return await provider.batch_completion(model_id, content_batch, sampling_params, response_format, logprobs)
 
     async def embeddings(
@@ -395,7 +395,8 @@ class InferenceRouter(Inference):
             raise ValueError(f"Model '{model_id}' not found")
         if model.model_type == ModelType.llm:
             raise ValueError(f"Model '{model_id}' is an LLM model and does not support embeddings")
-        return await self.routing_table.get_provider_impl(model_id).embeddings(
+        provider = await self.routing_table.get_provider_impl(model_id)
+        return await provider.embeddings(
             model_id=model_id,
             contents=contents,
             text_truncation=text_truncation,
@@ -458,7 +459,7 @@ class InferenceRouter(Inference):
             suffix=suffix,
         )
 
-        provider = self.routing_table.get_provider_impl(model_obj.identifier)
+        provider = await self.routing_table.get_provider_impl(model_obj.identifier)
         return await provider.openai_completion(**params)
 
     async def openai_chat_completion(
@@ -538,7 +539,7 @@ class InferenceRouter(Inference):
             user=user,
         )
 
-        provider = self.routing_table.get_provider_impl(model_obj.identifier)
+        provider = await self.routing_table.get_provider_impl(model_obj.identifier)
         if stream:
             response_stream = await provider.openai_chat_completion(**params)
             if self.store:
@@ -575,7 +576,7 @@ class InferenceRouter(Inference):
             user=user,
         )
 
-        provider = self.routing_table.get_provider_impl(model_obj.identifier)
+        provider = await self.routing_table.get_provider_impl(model_obj.identifier)
         return await provider.openai_embeddings(**params)
 
     async def list_chat_completions(

llama_stack/distribution/routers/safety.py

@@ -50,7 +50,8 @@ class SafetyRouter(Safety):
         params: dict[str, Any] = None,
     ) -> RunShieldResponse:
         logger.debug(f"SafetyRouter.run_shield: {shield_id}")
-        return await self.routing_table.get_provider_impl(shield_id).run_shield(
+        provider = await self.routing_table.get_provider_impl(shield_id)
+        return await provider.run_shield(
             shield_id=shield_id,
             messages=messages,
             params=params,

llama_stack/distribution/routers/tool_runtime.py

@@ -41,9 +41,8 @@ class ToolRuntimeRouter(ToolRuntime):
             query_config: RAGQueryConfig | None = None,
         ) -> RAGQueryResult:
             logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_db_ids}")
-            return await self.routing_table.get_provider_impl("knowledge_search").query(
-                content, vector_db_ids, query_config
-            )
+            provider = await self.routing_table.get_provider_impl("knowledge_search")
+            return await provider.query(content, vector_db_ids, query_config)
 
         async def insert(
             self,
@@ -54,9 +53,8 @@ class ToolRuntimeRouter(ToolRuntime):
             logger.debug(
                 f"ToolRuntimeRouter.RagToolImpl.insert: {vector_db_id}, {len(documents)} documents, chunk_size={chunk_size_in_tokens}"
             )
-            return await self.routing_table.get_provider_impl("insert_into_memory").insert(
-                documents, vector_db_id, chunk_size_in_tokens
-            )
+            provider = await self.routing_table.get_provider_impl("insert_into_memory")
+            return await provider.insert(documents, vector_db_id, chunk_size_in_tokens)
 
     def __init__(
         self,
@@ -80,7 +78,8 @@ class ToolRuntimeRouter(ToolRuntime):
 
     async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> Any:
         logger.debug(f"ToolRuntimeRouter.invoke_tool: {tool_name}")
-        return await self.routing_table.get_provider_impl(tool_name).invoke_tool(
+        provider = await self.routing_table.get_provider_impl(tool_name)
+        return await provider.invoke_tool(
             tool_name=tool_name,
             kwargs=kwargs,
         )

llama_stack/distribution/routers/vector_io.py

@@ -104,7 +104,8 @@ class VectorIORouter(VectorIO):
         logger.debug(
             f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.metadata['document_id'] for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}",
         )
-        return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(vector_db_id, chunks, ttl_seconds)
+        provider = await self.routing_table.get_provider_impl(vector_db_id)
+        return await provider.insert_chunks(vector_db_id, chunks, ttl_seconds)
 
     async def query_chunks(
         self,
@@ -113,7 +114,8 @@ class VectorIORouter(VectorIO):
         params: dict[str, Any] | None = None,
     ) -> QueryChunksResponse:
         logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}")
-        return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params)
+        provider = await self.routing_table.get_provider_impl(vector_db_id)
+        return await provider.query_chunks(vector_db_id, query, params)
 
     # OpenAI Vector Stores API endpoints
     async def openai_create_vector_store(
@@ -146,7 +148,8 @@ class VectorIORouter(VectorIO):
             provider_vector_db_id=vector_db_id,
             vector_db_name=name,
         )
-        return await self.routing_table.get_provider_impl(registered_vector_db.identifier).openai_create_vector_store(
+        provider = await self.routing_table.get_provider_impl(registered_vector_db.identifier)
+        return await provider.openai_create_vector_store(
             name=name,
             file_ids=file_ids,
             expires_after=expires_after,
@@ -172,9 +175,8 @@ class VectorIORouter(VectorIO):
         all_stores = []
         for vector_db in vector_dbs:
             try:
-                vector_store = await self.routing_table.get_provider_impl(
-                    vector_db.identifier
-                ).openai_retrieve_vector_store(vector_db.identifier)
+                provider = await self.routing_table.get_provider_impl(vector_db.identifier)
+                vector_store = await provider.openai_retrieve_vector_store(vector_db.identifier)
                 all_stores.append(vector_store)
             except Exception as e:
                 logger.error(f"Error retrieving vector store {vector_db.identifier}: {e}")

llama_stack/distribution/routing_tables/common.py

@@ -6,6 +6,7 @@
 
 from typing import Any
 
+from llama_stack.apis.models import Model
 from llama_stack.apis.resource import ResourceType
 from llama_stack.apis.scoring_functions import ScoringFn
 from llama_stack.distribution.access_control.access_control import AccessDeniedError, is_action_allowed
@@ -116,7 +117,7 @@ class CommonRoutingTableImpl(RoutingTable):
         for p in self.impls_by_provider_id.values():
             await p.shutdown()
 
-    def get_provider_impl(self, routing_key: str, provider_id: str | None = None) -> Any:
+    async def get_provider_impl(self, routing_key: str, provider_id: str | None = None) -> Any:
         from .benchmarks import BenchmarksRoutingTable
         from .datasets import DatasetsRoutingTable
         from .models import ModelsRoutingTable
@@ -235,3 +236,28 @@ class CommonRoutingTableImpl(RoutingTable):
             ]
 
         return filtered_objs
+
+
+async def lookup_model(routing_table: CommonRoutingTableImpl, model_id: str) -> Model:
+    # first try to get the model by identifier
+    # this works if model_id is an alias or is of the form provider_id/provider_model_id
+    model = await routing_table.get_object_by_identifier("model", model_id)
+    if model is not None:
+        return model
+
+    logger.warning(
+        f"WARNING: model identifier '{model_id}' not found in routing table. Falling back to "
+        "searching in all providers. This is only for backwards compatibility and will stop working "
+        "soon. Migrate your calls to use fully scoped `provider_id/model_id` names."
+    )
+    # if not found, this means model_id is an unscoped provider_model_id, we need
+    # to iterate (given a lack of an efficient index on the KVStore)
+    models = await routing_table.get_all_with_type("model")
+    matching_models = [m for m in models if m.provider_resource_id == model_id]
+    if len(matching_models) == 0:
+        raise ValueError(f"Model '{model_id}' not found")
+
+    if len(matching_models) > 1:
+        raise ValueError(f"Multiple providers found for '{model_id}': {[m.provider_id for m in matching_models]}")
+
+    return matching_models[0]

llama_stack/distribution/routing_tables/models.py

@@ -13,7 +13,7 @@ from llama_stack.distribution.datatypes import (
 )
 from llama_stack.log import get_logger
 
-from .common import CommonRoutingTableImpl
+from .common import CommonRoutingTableImpl, lookup_model
 
 logger = get_logger(name=__name__, category="core")
 
@@ -36,10 +36,11 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
         return OpenAIListModelsResponse(data=openai_models)
 
     async def get_model(self, model_id: str) -> Model:
-        model = await self.get_object_by_identifier("model", model_id)
-        if model is None:
-            raise ValueError(f"Model '{model_id}' not found")
-        return model
+        return await lookup_model(self, model_id)
+
+    async def get_provider_impl(self, model_id: str) -> Any:
+        model = await lookup_model(self, model_id)
+        return self.impls_by_provider_id[model.provider_id]
 
     async def register_model(
         self,
@@ -49,24 +50,33 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
         metadata: dict[str, Any] | None = None,
         model_type: ModelType | None = None,
     ) -> Model:
-        if provider_model_id is None:
-            provider_model_id = model_id
         if provider_id is None:
             # If provider_id not specified, use the only provider if it supports this model
             if len(self.impls_by_provider_id) == 1:
                 provider_id = list(self.impls_by_provider_id.keys())[0]
             else:
                 raise ValueError(
-                    f"No provider specified and multiple providers available. Please specify a provider_id. Available providers: {self.impls_by_provider_id.keys()}"
+                    f"Please specify a provider_id for model {model_id} since multiple providers are available: {self.impls_by_provider_id.keys()}.\n\n"
+                    "Use the provider_id as a prefix to disambiguate, e.g. 'provider_id/model_id'."
                 )
-        if metadata is None:
-            metadata = {}
-        if model_type is None:
-            model_type = ModelType.llm
+
+        provider_model_id = provider_model_id or model_id
+        metadata = metadata or {}
+        model_type = model_type or ModelType.llm
         if "embedding_dimension" not in metadata and model_type == ModelType.embedding:
             raise ValueError("Embedding model must have an embedding dimension in its metadata")
+
+        # an identifier different than provider_model_id implies it is an alias, so that
+        # becomes the globally unique identifier. otherwise provider_model_ids can conflict,
+        # so as a general rule we must use the provider_id to disambiguate.
+
+        if model_id != provider_model_id:
+            identifier = model_id
+        else:
+            identifier = f"{provider_id}/{provider_model_id}"
+
         model = ModelWithOwner(
-            identifier=model_id,
+            identifier=identifier,
             provider_resource_id=provider_model_id,
             provider_id=provider_id,
             metadata=metadata,

llama_stack/distribution/routing_tables/toolgroups.py

@@ -30,7 +30,7 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
     tool_to_toolgroup: dict[str, str] = {}
 
     # overridden
-    def get_provider_impl(self, routing_key: str, provider_id: str | None = None) -> Any:
+    async def get_provider_impl(self, routing_key: str, provider_id: str | None = None) -> Any:
         # we don't index tools in the registry anymore, but only keep a cache of them by toolgroup_id
         # TODO: we may want to invalidate the cache (for a given toolgroup_id) every once in a while?
 
@@ -40,7 +40,7 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
 
         if routing_key in self.tool_to_toolgroup:
             routing_key = self.tool_to_toolgroup[routing_key]
-        return super().get_provider_impl(routing_key, provider_id)
+        return await super().get_provider_impl(routing_key, provider_id)
 
     async def list_tools(self, toolgroup_id: str | None = None) -> ListToolsResponse:
         if toolgroup_id:
@@ -59,7 +59,7 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
         return ListToolsResponse(data=all_tools)
 
     async def _index_tools(self, toolgroup: ToolGroup):
-        provider_impl = super().get_provider_impl(toolgroup.identifier, toolgroup.provider_id)
+        provider_impl = await super().get_provider_impl(toolgroup.identifier, toolgroup.provider_id)
         tooldefs_response = await provider_impl.list_runtime_tools(toolgroup.identifier, toolgroup.mcp_endpoint)
 
         # TODO: kill this Tool vs ToolDef distinction

llama_stack/distribution/routing_tables/vector_dbs.py

@@ -27,7 +27,7 @@ from llama_stack.distribution.datatypes import (
 )
 from llama_stack.log import get_logger
 
-from .common import CommonRoutingTableImpl
+from .common import CommonRoutingTableImpl, lookup_model
 
 logger = get_logger(name=__name__, category="core")
 
@@ -51,8 +51,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         provider_vector_db_id: str | None = None,
         vector_db_name: str | None = None,
     ) -> VectorDB:
-        if provider_vector_db_id is None:
-            provider_vector_db_id = vector_db_id
+        provider_vector_db_id = provider_vector_db_id or vector_db_id
         if provider_id is None:
             if len(self.impls_by_provider_id) > 0:
                 provider_id = list(self.impls_by_provider_id.keys())[0]
@@ -62,7 +61,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
                     )
             else:
                 raise ValueError("No provider available. Please configure a vector_io provider.")
-        model = await self.get_object_by_identifier("model", embedding_model)
+        model = await lookup_model(self, embedding_model)
         if model is None:
             raise ValueError(f"Model {embedding_model} not found")
         if model.model_type != ModelType.embedding:
@@ -93,7 +92,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         vector_store_id: str,
     ) -> VectorStoreObject:
         await self.assert_action_allowed("read", "vector_db", vector_store_id)
-        return await self.get_provider_impl(vector_store_id).openai_retrieve_vector_store(vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store(vector_store_id)
 
     async def openai_update_vector_store(
         self,
@@ -103,7 +103,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         metadata: dict[str, Any] | None = None,
     ) -> VectorStoreObject:
         await self.assert_action_allowed("update", "vector_db", vector_store_id)
-        return await self.get_provider_impl(vector_store_id).openai_update_vector_store(
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_update_vector_store(
             vector_store_id=vector_store_id,
             name=name,
             expires_after=expires_after,
@@ -115,7 +116,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         vector_store_id: str,
     ) -> VectorStoreDeleteResponse:
         await self.assert_action_allowed("delete", "vector_db", vector_store_id)
-        result = await self.get_provider_impl(vector_store_id).openai_delete_vector_store(vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        result = await provider.openai_delete_vector_store(vector_store_id)
         await self.unregister_vector_db(vector_store_id)
         return result
 
@@ -130,7 +132,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         search_mode: str | None = "vector",
     ) -> VectorStoreSearchResponsePage:
         await self.assert_action_allowed("read", "vector_db", vector_store_id)
-        return await self.get_provider_impl(vector_store_id).openai_search_vector_store(
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_search_vector_store(
             vector_store_id=vector_store_id,
             query=query,
             filters=filters,
@@ -148,7 +151,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         chunking_strategy: VectorStoreChunkingStrategy | None = None,
     ) -> VectorStoreFileObject:
         await self.assert_action_allowed("update", "vector_db", vector_store_id)
-        return await self.get_provider_impl(vector_store_id).openai_attach_file_to_vector_store(
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_attach_file_to_vector_store(
             vector_store_id=vector_store_id,
             file_id=file_id,
             attributes=attributes,
@@ -165,7 +169,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         filter: VectorStoreFileStatus | None = None,
     ) -> list[VectorStoreFileObject]:
         await self.assert_action_allowed("read", "vector_db", vector_store_id)
-        return await self.get_provider_impl(vector_store_id).openai_list_files_in_vector_store(
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_list_files_in_vector_store(
             vector_store_id=vector_store_id,
             limit=limit,
             order=order,
@@ -180,7 +185,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         file_id: str,
     ) -> VectorStoreFileObject:
         await self.assert_action_allowed("read", "vector_db", vector_store_id)
-        return await self.get_provider_impl(vector_store_id).openai_retrieve_vector_store_file(
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file(
             vector_store_id=vector_store_id,
             file_id=file_id,
         )
@@ -191,7 +197,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         file_id: str,
     ) -> VectorStoreFileContentsResponse:
         await self.assert_action_allowed("read", "vector_db", vector_store_id)
-        return await self.get_provider_impl(vector_store_id).openai_retrieve_vector_store_file_contents(
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file_contents(
             vector_store_id=vector_store_id,
             file_id=file_id,
         )
@@ -203,7 +210,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         attributes: dict[str, Any],
     ) -> VectorStoreFileObject:
         await self.assert_action_allowed("update", "vector_db", vector_store_id)
-        return await self.get_provider_impl(vector_store_id).openai_update_vector_store_file(
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_update_vector_store_file(
             vector_store_id=vector_store_id,
             file_id=file_id,
             attributes=attributes,
@@ -215,7 +223,8 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         file_id: str,
     ) -> VectorStoreFileDeleteResponse:
         await self.assert_action_allowed("delete", "vector_db", vector_store_id)
-        return await self.get_provider_impl(vector_store_id).openai_delete_vector_store_file(
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_delete_vector_store_file(
             vector_store_id=vector_store_id,
             file_id=file_id,
         )

llama_stack/distribution/server/server.py

@@ -32,7 +32,7 @@ from openai import BadRequestError
 from pydantic import BaseModel, ValidationError
 
 from llama_stack.apis.common.responses import PaginatedResponse
-from llama_stack.cli.utils import add_config_template_args
+from llama_stack.cli.utils import add_config_template_args, get_config_from_args
 from llama_stack.distribution.access_control.access_control import AccessDeniedError
 from llama_stack.distribution.datatypes import (
     AuthenticationRequiredError,
@@ -399,7 +399,8 @@ def main(args: argparse.Namespace | None = None):
     if args is None:
         args = parser.parse_args()
 
-    config_file = resolve_config_or_template(args.config, Mode.RUN)
+    config_or_template = get_config_from_args(args)
+    config_file = resolve_config_or_template(config_or_template, Mode.RUN)
 
     logger_config = None
     with open(config_file) as fp:

llama_stack/providers/datatypes.py

@@ -113,7 +113,7 @@ class ProviderSpec(BaseModel):
 
 
 class RoutingTable(Protocol):
-    def get_provider_impl(self, routing_key: str) -> Any: ...
+    async def get_provider_impl(self, routing_key: str) -> Any: ...
 
 
 # TODO: this can now be inlined into RemoteProviderSpec

llama_stack/providers/remote/inference/llama_openai_compat/llama.py

@@ -5,17 +5,27 @@
 # the root directory of this source tree.
 import logging
 
-from llama_api_client import AsyncLlamaAPIClient, NotFoundError
-
 from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .models import MODEL_ENTRIES
 
 logger = logging.getLogger(__name__)
 
 
-class LlamaCompatInferenceAdapter(LiteLLMOpenAIMixin):
+class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+    """
+    Llama API Inference Adapter for Llama Stack.
+
+    Note: The inheritance order is important here. OpenAIMixin must come before
+    LiteLLMOpenAIMixin to ensure that OpenAIMixin.check_model_availability()
+    is used instead of ModelRegistryHelper.check_model_availability().
+
+    - OpenAIMixin.check_model_availability() queries the Llama API to check if a model exists
+    - ModelRegistryHelper.check_model_availability() (inherited by LiteLLMOpenAIMixin) just returns False and shows a warning
+    """
+
     _config: LlamaCompatConfig
 
     def __init__(self, config: LlamaCompatConfig):
@@ -28,32 +38,19 @@ class LlamaCompatInferenceAdapter(LiteLLMOpenAIMixin):
         )
         self.config = config
 
-    async def check_model_availability(self, model: str) -> bool:
+    # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin
+    get_api_key = LiteLLMOpenAIMixin.get_api_key
+
+    def get_base_url(self) -> str:
         """
-        Check if a specific model is available from Llama API.
+        Get the base URL for OpenAI mixin.
 
-        :param model: The model identifier to check.
-        :return: True if the model is available dynamically, False otherwise.
+        :return: The Llama API base URL
         """
-        try:
-            llama_api_client = self._get_llama_api_client()
-            retrieved_model = await llama_api_client.models.retrieve(model)
-            logger.info(f"Model {retrieved_model.id} is available from Llama API")
-            return True
-
-        except NotFoundError:
-            logger.error(f"Model {model} is not available from Llama API")
-            return False
-
-        except Exception as e:
-            logger.error(f"Failed to check model availability from Llama API: {e}")
-            return False
+        return self.config.openai_compat_api_base
 
     async def initialize(self):
         await super().initialize()
 
     async def shutdown(self):
         await super().shutdown()
-
-    def _get_llama_api_client(self) -> AsyncLlamaAPIClient:
-        return AsyncLlamaAPIClient(api_key=self.get_api_key(), base_url=self.config.openai_compat_api_base)

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -7,9 +7,8 @@
 import logging
 import warnings
 from collections.abc import AsyncIterator
-from typing import Any
 
-from openai import APIConnectionError, AsyncOpenAI, BadRequestError, NotFoundError
+from openai import APIConnectionError, BadRequestError
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -28,12 +27,6 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
-    OpenAIChatCompletion,
-    OpenAIChatCompletionChunk,
-    OpenAICompletion,
-    OpenAIEmbeddingsResponse,
-    OpenAIMessageParam,
-    OpenAIResponseFormatParam,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -47,8 +40,8 @@ from llama_stack.providers.utils.inference.model_registry import (
 from llama_stack.providers.utils.inference.openai_compat import (
     convert_openai_chat_completion_choice,
     convert_openai_chat_completion_stream,
-    prepare_openai_completion_params,
 )
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from llama_stack.providers.utils.inference.prompt_adapter import content_has_media
 
 from . import NVIDIAConfig
@@ -64,7 +57,20 @@ from .utils import _is_nvidia_hosted
 logger = logging.getLogger(__name__)
 
 
-class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
+class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
+    """
+    NVIDIA Inference Adapter for Llama Stack.
+
+    Note: The inheritance order is important here. OpenAIMixin must come before
+    ModelRegistryHelper to ensure that OpenAIMixin.check_model_availability()
+    is used instead of ModelRegistryHelper.check_model_availability(). It also
+    must come before Inference to ensure that OpenAIMixin methods are available
+    in the Inference interface.
+
+    - OpenAIMixin.check_model_availability() queries the NVIDIA API to check if a model exists
+    - ModelRegistryHelper.check_model_availability() just returns False and shows a warning
+    """
+
     def __init__(self, config: NVIDIAConfig) -> None:
         # TODO(mf): filter by available models
         ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
@@ -88,45 +94,21 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
 
         self._config = config
 
-    async def check_model_availability(self, model: str) -> bool:
+    def get_api_key(self) -> str:
         """
-        Check if a specific model is available.
+        Get the API key for OpenAI mixin.
 
-        :param model: The model identifier to check.
-        :return: True if the model is available dynamically, False otherwise.
+        :return: The NVIDIA API key
         """
-        try:
-            await self._client.models.retrieve(model)
-            return True
-        except NotFoundError:
-            logger.error(f"Model {model} is not available")
-        except Exception as e:
-            logger.error(f"Failed to check model availability: {e}")
-        return False
+        return self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"
 
-    @property
-    def _client(self) -> AsyncOpenAI:
+    def get_base_url(self) -> str:
         """
-        Returns an OpenAI client for the configured NVIDIA API endpoint.
+        Get the base URL for OpenAI mixin.
 
-        :return: An OpenAI client
+        :return: The NVIDIA API base URL
         """
-
-        base_url = f"{self._config.url}/v1" if self._config.append_api_version else self._config.url
-
-        return AsyncOpenAI(
-            base_url=base_url,
-            api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
-            timeout=self._config.timeout,
-        )
-
-    async def _get_provider_model_id(self, model_id: str) -> str:
-        if not self.model_store:
-            raise RuntimeError("Model store is not set")
-        model = await self.model_store.get_model(model_id)
-        if model is None:
-            raise ValueError(f"Model {model_id} is unknown")
-        return model.provider_model_id
+        return f"{self._config.url}/v1" if self._config.append_api_version else self._config.url
 
     async def completion(
         self,
@@ -160,7 +142,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         )
 
         try:
-            response = await self._client.completions.create(**request)
+            response = await self.client.completions.create(**request)
         except APIConnectionError as e:
             raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
 
@@ -213,7 +195,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
             extra_body["input_type"] = task_type_options[task_type]
 
         try:
-            response = await self._client.embeddings.create(
+            response = await self.client.embeddings.create(
                 model=provider_model_id,
                 input=input,
                 extra_body=extra_body,
@@ -228,16 +210,6 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         #
         return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data])
 
-    async def openai_embeddings(
-        self,
-        model: str,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
-    ) -> OpenAIEmbeddingsResponse:
-        raise NotImplementedError()
-
     async def chat_completion(
         self,
         model_id: str,
@@ -274,7 +246,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         )
 
         try:
-            response = await self._client.chat.completions.create(**request)
+            response = await self.client.chat.completions.create(**request)
         except APIConnectionError as e:
             raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
 
@@ -283,112 +255,3 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         else:
             # we pass n=1 to get only one completion
             return convert_openai_chat_completion_choice(response.choices[0])
-
-    async def openai_completion(
-        self,
-        model: str,
-        prompt: str | list[str] | list[int] | list[list[int]],
-        best_of: int | None = None,
-        echo: bool | None = None,
-        frequency_penalty: float | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        presence_penalty: float | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-        guided_choice: list[str] | None = None,
-        prompt_logprobs: int | None = None,
-        suffix: str | None = None,
-    ) -> OpenAICompletion:
-        provider_model_id = await self._get_provider_model_id(model)
-
-        params = await prepare_openai_completion_params(
-            model=provider_model_id,
-            prompt=prompt,
-            best_of=best_of,
-            echo=echo,
-            frequency_penalty=frequency_penalty,
-            logit_bias=logit_bias,
-            logprobs=logprobs,
-            max_tokens=max_tokens,
-            n=n,
-            presence_penalty=presence_penalty,
-            seed=seed,
-            stop=stop,
-            stream=stream,
-            stream_options=stream_options,
-            temperature=temperature,
-            top_p=top_p,
-            user=user,
-        )
-
-        try:
-            return await self._client.completions.create(**params)
-        except APIConnectionError as e:
-            raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
-
-    async def openai_chat_completion(
-        self,
-        model: str,
-        messages: list[OpenAIMessageParam],
-        frequency_penalty: float | None = None,
-        function_call: str | dict[str, Any] | None = None,
-        functions: list[dict[str, Any]] | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_completion_tokens: int | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        parallel_tool_calls: bool | None = None,
-        presence_penalty: float | None = None,
-        response_format: OpenAIResponseFormatParam | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
-        tools: list[dict[str, Any]] | None = None,
-        top_logprobs: int | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        provider_model_id = await self._get_provider_model_id(model)
-
-        params = await prepare_openai_completion_params(
-            model=provider_model_id,
-            messages=messages,
-            frequency_penalty=frequency_penalty,
-            function_call=function_call,
-            functions=functions,
-            logit_bias=logit_bias,
-            logprobs=logprobs,
-            max_completion_tokens=max_completion_tokens,
-            max_tokens=max_tokens,
-            n=n,
-            parallel_tool_calls=parallel_tool_calls,
-            presence_penalty=presence_penalty,
-            response_format=response_format,
-            seed=seed,
-            stop=stop,
-            stream=stream,
-            stream_options=stream_options,
-            temperature=temperature,
-            tool_choice=tool_choice,
-            tools=tools,
-            top_logprobs=top_logprobs,
-            top_p=top_p,
-            user=user,
-        )
-
-        try:
-            return await self._client.chat.completions.create(**params)
-        except APIConnectionError as e:
-            raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e

llama_stack/providers/remote/inference/openai/openai.py

@@ -5,23 +5,9 @@
 # the root directory of this source tree.
 
 import logging
-from collections.abc import AsyncIterator
-from typing import Any
 
-from openai import AsyncOpenAI, NotFoundError
-
-from llama_stack.apis.inference import (
-    OpenAIChatCompletion,
-    OpenAIChatCompletionChunk,
-    OpenAICompletion,
-    OpenAIEmbeddingData,
-    OpenAIEmbeddingsResponse,
-    OpenAIEmbeddingUsage,
-    OpenAIMessageParam,
-    OpenAIResponseFormatParam,
-)
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
-from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import OpenAIConfig
 from .models import MODEL_ENTRIES
@@ -30,7 +16,7 @@ logger = logging.getLogger(__name__)
 
 
 #
-# This OpenAI adapter implements Inference methods using two clients -
+# This OpenAI adapter implements Inference methods using two mixins -
 #
 # | Inference Method           | Implementation Source    |
 # |----------------------------|--------------------------|
@@ -39,11 +25,22 @@ logger = logging.getLogger(__name__)
 # | embedding                  | LiteLLMOpenAIMixin       |
 # | batch_completion           | LiteLLMOpenAIMixin       |
 # | batch_chat_completion      | LiteLLMOpenAIMixin       |
-# | openai_completion          | AsyncOpenAI              |
-# | openai_chat_completion     | AsyncOpenAI              |
-# | openai_embeddings          | AsyncOpenAI              |
+# | openai_completion          | OpenAIMixin              |
+# | openai_chat_completion     | OpenAIMixin              |
+# | openai_embeddings          | OpenAIMixin              |
 #
-class OpenAIInferenceAdapter(LiteLLMOpenAIMixin):
+class OpenAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+    """
+    OpenAI Inference Adapter for Llama Stack.
+
+    Note: The inheritance order is important here. OpenAIMixin must come before
+    LiteLLMOpenAIMixin to ensure that OpenAIMixin.check_model_availability()
+    is used instead of ModelRegistryHelper.check_model_availability().
+
+    - OpenAIMixin.check_model_availability() queries the OpenAI API to check if a model exists
+    - ModelRegistryHelper.check_model_availability() (inherited by LiteLLMOpenAIMixin) just returns False and shows a warning
+    """
+
     def __init__(self, config: OpenAIConfig) -> None:
         LiteLLMOpenAIMixin.__init__(
             self,
@@ -60,191 +57,19 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin):
         # litellm specific model names, an abstraction leak.
         self.is_openai_compat = True
 
-    async def check_model_availability(self, model: str) -> bool:
+    # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin
+    get_api_key = LiteLLMOpenAIMixin.get_api_key
+
+    def get_base_url(self) -> str:
         """
-        Check if a specific model is available from OpenAI.
+        Get the OpenAI API base URL.
 
-        :param model: The model identifier to check.
-        :return: True if the model is available dynamically, False otherwise.
+        Returns the standard OpenAI API base URL for direct OpenAI API calls.
         """
-        try:
-            openai_client = self._get_openai_client()
-            retrieved_model = await openai_client.models.retrieve(model)
-            logger.info(f"Model {retrieved_model.id} is available from OpenAI")
-            return True
-
-        except NotFoundError:
-            logger.error(f"Model {model} is not available from OpenAI")
-            return False
-
-        except Exception as e:
-            logger.error(f"Failed to check model availability from OpenAI: {e}")
-            return False
+        return "https://api.openai.com/v1"
 
     async def initialize(self) -> None:
         await super().initialize()
 
     async def shutdown(self) -> None:
         await super().shutdown()
-
-    def _get_openai_client(self) -> AsyncOpenAI:
-        return AsyncOpenAI(
-            api_key=self.get_api_key(),
-        )
-
-    async def openai_completion(
-        self,
-        model: str,
-        prompt: str | list[str] | list[int] | list[list[int]],
-        best_of: int | None = None,
-        echo: bool | None = None,
-        frequency_penalty: float | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        presence_penalty: float | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-        guided_choice: list[str] | None = None,
-        prompt_logprobs: int | None = None,
-        suffix: str | None = None,
-    ) -> OpenAICompletion:
-        if guided_choice is not None:
-            logging.warning("guided_choice is not supported by the OpenAI API. Ignoring.")
-        if prompt_logprobs is not None:
-            logging.warning("prompt_logprobs is not supported by the OpenAI API. Ignoring.")
-
-        model_id = (await self.model_store.get_model(model)).provider_resource_id
-        if model_id.startswith("openai/"):
-            model_id = model_id[len("openai/") :]
-        params = await prepare_openai_completion_params(
-            model=model_id,
-            prompt=prompt,
-            best_of=best_of,
-            echo=echo,
-            frequency_penalty=frequency_penalty,
-            logit_bias=logit_bias,
-            logprobs=logprobs,
-            max_tokens=max_tokens,
-            n=n,
-            presence_penalty=presence_penalty,
-            seed=seed,
-            stop=stop,
-            stream=stream,
-            stream_options=stream_options,
-            temperature=temperature,
-            top_p=top_p,
-            user=user,
-            suffix=suffix,
-        )
-        return await self._get_openai_client().completions.create(**params)
-
-    async def openai_chat_completion(
-        self,
-        model: str,
-        messages: list[OpenAIMessageParam],
-        frequency_penalty: float | None = None,
-        function_call: str | dict[str, Any] | None = None,
-        functions: list[dict[str, Any]] | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_completion_tokens: int | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        parallel_tool_calls: bool | None = None,
-        presence_penalty: float | None = None,
-        response_format: OpenAIResponseFormatParam | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
-        tools: list[dict[str, Any]] | None = None,
-        top_logprobs: int | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        model_id = (await self.model_store.get_model(model)).provider_resource_id
-        if model_id.startswith("openai/"):
-            model_id = model_id[len("openai/") :]
-        params = await prepare_openai_completion_params(
-            model=model_id,
-            messages=messages,
-            frequency_penalty=frequency_penalty,
-            function_call=function_call,
-            functions=functions,
-            logit_bias=logit_bias,
-            logprobs=logprobs,
-            max_completion_tokens=max_completion_tokens,
-            max_tokens=max_tokens,
-            n=n,
-            parallel_tool_calls=parallel_tool_calls,
-            presence_penalty=presence_penalty,
-            response_format=response_format,
-            seed=seed,
-            stop=stop,
-            stream=stream,
-            stream_options=stream_options,
-            temperature=temperature,
-            tool_choice=tool_choice,
-            tools=tools,
-            top_logprobs=top_logprobs,
-            top_p=top_p,
-            user=user,
-        )
-        return await self._get_openai_client().chat.completions.create(**params)
-
-    async def openai_embeddings(
-        self,
-        model: str,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
-    ) -> OpenAIEmbeddingsResponse:
-        model_id = (await self.model_store.get_model(model)).provider_resource_id
-        if model_id.startswith("openai/"):
-            model_id = model_id[len("openai/") :]
-
-        # Prepare parameters for OpenAI embeddings API
-        params = {
-            "model": model_id,
-            "input": input,
-        }
-
-        if encoding_format is not None:
-            params["encoding_format"] = encoding_format
-        if dimensions is not None:
-            params["dimensions"] = dimensions
-        if user is not None:
-            params["user"] = user
-
-        # Call OpenAI embeddings API
-        response = await self._get_openai_client().embeddings.create(**params)
-
-        data = []
-        for i, embedding_data in enumerate(response.data):
-            data.append(
-                OpenAIEmbeddingData(
-                    embedding=embedding_data.embedding,
-                    index=i,
-                )
-            )
-
-        usage = OpenAIEmbeddingUsage(
-            prompt_tokens=response.usage.prompt_tokens,
-            total_tokens=response.usage.total_tokens,
-        )
-
-        return OpenAIEmbeddingsResponse(
-            data=data,
-            model=response.model,
-            usage=usage,
-        )

llama_stack/providers/utils/inference/embedding_mixin.py

@@ -88,7 +88,7 @@ class SentenceTransformerEmbeddingMixin:
         usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
         return OpenAIEmbeddingsResponse(
             data=data,
-            model=model_obj.provider_resource_id,
+            model=model,
             usage=usage,
         )
 

llama_stack/providers/utils/inference/model_registry.py

@@ -10,12 +10,15 @@ from pydantic import BaseModel, Field
 
 from llama_stack.apis.common.errors import UnsupportedModelError
 from llama_stack.apis.models import ModelType
+from llama_stack.log import get_logger
 from llama_stack.models.llama.sku_list import all_registered_models
 from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
 from llama_stack.providers.utils.inference import (
     ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR,
 )
 
+logger = get_logger(name=__name__, category="core")
+
 
 # TODO: this class is more confusing than useful right now. We need to make it
 # more closer to the Model class.
@@ -98,6 +101,9 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
         :param model: The model identifier to check.
         :return: True if the model is available dynamically, False otherwise.
         """
+        logger.info(
+            f"check_model_availability is not implemented for {self.__class__.__name__}. Returning False by default."
+        )
         return False
 
     async def register_model(self, model: Model) -> Model:

llama_stack/providers/utils/inference/openai_mixin.py

@@ -0,0 +1,272 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+from collections.abc import AsyncIterator
+from typing import Any
+
+import openai
+from openai import NOT_GIVEN, AsyncOpenAI
+
+from llama_stack.apis.inference import (
+    Model,
+    OpenAIChatCompletion,
+    OpenAIChatCompletionChunk,
+    OpenAICompletion,
+    OpenAIEmbeddingData,
+    OpenAIEmbeddingsResponse,
+    OpenAIEmbeddingUsage,
+    OpenAIMessageParam,
+    OpenAIResponseFormatParam,
+)
+from llama_stack.log import get_logger
+from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
+
+logger = get_logger(name=__name__, category="core")
+
+
+class OpenAIMixin(ABC):
+    """
+    Mixin class that provides OpenAI-specific functionality for inference providers.
+    This class handles direct OpenAI API calls using the AsyncOpenAI client.
+
+    This is an abstract base class that requires child classes to implement:
+    - get_api_key(): Method to retrieve the API key
+    - get_base_url(): Method to retrieve the OpenAI-compatible API base URL
+
+    Expected Dependencies:
+    - self.model_store: Injected by the Llama Stack distribution system at runtime.
+      This provides model registry functionality for looking up registered models.
+      The model_store is set in routing_tables/common.py during provider initialization.
+    """
+
+    @abstractmethod
+    def get_api_key(self) -> str:
+        """
+        Get the API key.
+
+        This method must be implemented by child classes to provide the API key
+        for authenticating with the OpenAI API or compatible endpoints.
+
+        :return: The API key as a string
+        """
+        pass
+
+    @abstractmethod
+    def get_base_url(self) -> str:
+        """
+        Get the OpenAI-compatible API base URL.
+
+        This method must be implemented by child classes to provide the base URL
+        for the OpenAI API or compatible endpoints (e.g., "https://api.openai.com/v1").
+
+        :return: The base URL as a string
+        """
+        pass
+
+    @property
+    def client(self) -> AsyncOpenAI:
+        """
+        Get an AsyncOpenAI client instance.
+
+        Uses the abstract methods get_api_key() and get_base_url() which must be
+        implemented by child classes.
+        """
+        return AsyncOpenAI(
+            api_key=self.get_api_key(),
+            base_url=self.get_base_url(),
+        )
+
+    async def _get_provider_model_id(self, model: str) -> str:
+        """
+        Get the provider-specific model ID from the model store.
+
+        This is a utility method that looks up the registered model and returns
+        the provider_resource_id that should be used for actual API calls.
+
+        :param model: The registered model name/identifier
+        :return: The provider-specific model ID (e.g., "gpt-4")
+        """
+        # Look up the registered model to get the provider-specific model ID
+        # self.model_store is injected by the distribution system at runtime
+        model_obj: Model = await self.model_store.get_model(model)  # type: ignore[attr-defined]
+        # provider_resource_id is str | None, but we expect it to be str for OpenAI calls
+        if model_obj.provider_resource_id is None:
+            raise ValueError(f"Model {model} has no provider_resource_id")
+        return model_obj.provider_resource_id
+
+    async def openai_completion(
+        self,
+        model: str,
+        prompt: str | list[str] | list[int] | list[list[int]],
+        best_of: int | None = None,
+        echo: bool | None = None,
+        frequency_penalty: float | None = None,
+        logit_bias: dict[str, float] | None = None,
+        logprobs: bool | None = None,
+        max_tokens: int | None = None,
+        n: int | None = None,
+        presence_penalty: float | None = None,
+        seed: int | None = None,
+        stop: str | list[str] | None = None,
+        stream: bool | None = None,
+        stream_options: dict[str, Any] | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        user: str | None = None,
+        guided_choice: list[str] | None = None,
+        prompt_logprobs: int | None = None,
+        suffix: str | None = None,
+    ) -> OpenAICompletion:
+        """
+        Direct OpenAI completion API call.
+        """
+        if guided_choice is not None:
+            logger.warning("guided_choice is not supported by the OpenAI API. Ignoring.")
+        if prompt_logprobs is not None:
+            logger.warning("prompt_logprobs is not supported by the OpenAI API. Ignoring.")
+
+        # TODO: fix openai_completion to return type compatible with OpenAI's API response
+        return await self.client.completions.create(  # type: ignore[no-any-return]
+            **await prepare_openai_completion_params(
+                model=await self._get_provider_model_id(model),
+                prompt=prompt,
+                best_of=best_of,
+                echo=echo,
+                frequency_penalty=frequency_penalty,
+                logit_bias=logit_bias,
+                logprobs=logprobs,
+                max_tokens=max_tokens,
+                n=n,
+                presence_penalty=presence_penalty,
+                seed=seed,
+                stop=stop,
+                stream=stream,
+                stream_options=stream_options,
+                temperature=temperature,
+                top_p=top_p,
+                user=user,
+                suffix=suffix,
+            )
+        )
+
+    async def openai_chat_completion(
+        self,
+        model: str,
+        messages: list[OpenAIMessageParam],
+        frequency_penalty: float | None = None,
+        function_call: str | dict[str, Any] | None = None,
+        functions: list[dict[str, Any]] | None = None,
+        logit_bias: dict[str, float] | None = None,
+        logprobs: bool | None = None,
+        max_completion_tokens: int | None = None,
+        max_tokens: int | None = None,
+        n: int | None = None,
+        parallel_tool_calls: bool | None = None,
+        presence_penalty: float | None = None,
+        response_format: OpenAIResponseFormatParam | None = None,
+        seed: int | None = None,
+        stop: str | list[str] | None = None,
+        stream: bool | None = None,
+        stream_options: dict[str, Any] | None = None,
+        temperature: float | None = None,
+        tool_choice: str | dict[str, Any] | None = None,
+        tools: list[dict[str, Any]] | None = None,
+        top_logprobs: int | None = None,
+        top_p: float | None = None,
+        user: str | None = None,
+    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
+        """
+        Direct OpenAI chat completion API call.
+        """
+        # Type ignore because return types are compatible
+        return await self.client.chat.completions.create(  # type: ignore[no-any-return]
+            **await prepare_openai_completion_params(
+                model=await self._get_provider_model_id(model),
+                messages=messages,
+                frequency_penalty=frequency_penalty,
+                function_call=function_call,
+                functions=functions,
+                logit_bias=logit_bias,
+                logprobs=logprobs,
+                max_completion_tokens=max_completion_tokens,
+                max_tokens=max_tokens,
+                n=n,
+                parallel_tool_calls=parallel_tool_calls,
+                presence_penalty=presence_penalty,
+                response_format=response_format,
+                seed=seed,
+                stop=stop,
+                stream=stream,
+                stream_options=stream_options,
+                temperature=temperature,
+                tool_choice=tool_choice,
+                tools=tools,
+                top_logprobs=top_logprobs,
+                top_p=top_p,
+                user=user,
+            )
+        )
+
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        """
+        Direct OpenAI embeddings API call.
+        """
+        # Call OpenAI embeddings API with properly typed parameters
+        response = await self.client.embeddings.create(
+            model=await self._get_provider_model_id(model),
+            input=input,
+            encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
+            dimensions=dimensions if dimensions is not None else NOT_GIVEN,
+            user=user if user is not None else NOT_GIVEN,
+        )
+
+        data = []
+        for i, embedding_data in enumerate(response.data):
+            data.append(
+                OpenAIEmbeddingData(
+                    embedding=embedding_data.embedding,
+                    index=i,
+                )
+            )
+
+        usage = OpenAIEmbeddingUsage(
+            prompt_tokens=response.usage.prompt_tokens,
+            total_tokens=response.usage.total_tokens,
+        )
+
+        return OpenAIEmbeddingsResponse(
+            data=data,
+            model=response.model,
+            usage=usage,
+        )
+
+    async def check_model_availability(self, model: str) -> bool:
+        """
+        Check if a specific model is available from OpenAI.
+
+        :param model: The model identifier to check.
+        :return: True if the model is available dynamically, False otherwise.
+        """
+        try:
+            # Direct model lookup - returns model or raises NotFoundError
+            await self.client.models.retrieve(model)
+            return True
+        except openai.NotFoundError:
+            # Model doesn't exist - this is expected for unavailable models
+            pass
+        except Exception as e:
+            # All other errors (auth, rate limit, network, etc.)
+            logger.warning(f"Failed to check model availability for {model}: {e}")
+
+        return False