Merge 92d934e476 into 40fdce79b3

llama_stack/exceptions.py

@@ -0,0 +1,13 @@
+# 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.
+
+
+class UnsupportedModelError(ValueError):
+    """raised when model is not present in the list of supported models"""
+
+    def __init__(self, model_name: str, supported_models_list: list[str]):
+        message = f"'{model_name}' model is not supported. Supported models are: {', '.join(supported_models_list)}"
+        super().__init__(message)

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -48,6 +48,7 @@ from llama_stack.apis.inference import (
     ToolPromptFormat,
 )
 from llama_stack.apis.models import Model, ModelType
+from llama_stack.exceptions import UnsupportedModelError
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import (
     HealthResponse,
@@ -376,9 +377,7 @@ class OllamaInferenceAdapter(
                     f"Imprecise provider resource id was used but 'latest' is available in Ollama - using '{model.provider_resource_id}:latest'"
                 )
                 return model
-            raise ValueError(
+            raise UnsupportedModelError(model.provider_resource_id, available_models)
-                f"Model '{model.provider_resource_id}' is not available in Ollama. Available models: {', '.join(available_models)}"
-            )
         model.provider_resource_id = provider_resource_id
 
         return model

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -40,6 +40,7 @@ from llama_stack.apis.inference import (
 )
 from llama_stack.apis.models import Model
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
+from llama_stack.exceptions import UnsupportedModelError
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.openai_compat import (
@@ -92,7 +93,7 @@ class LiteLLMOpenAIMixin(
     async def register_model(self, model: Model) -> Model:
         model_id = self.get_provider_model_id(model.provider_resource_id)
         if model_id is None:
-            raise ValueError(f"Unsupported model: {model.provider_resource_id}")
+            raise UnsupportedModelError(model.provider_resource_id, self.alias_to_provider_id_map.keys())
         return model
 
     def get_litellm_model_name(self, model_id: str) -> str:

llama_stack/providers/utils/inference/model_registry.py

@@ -9,6 +9,7 @@ from typing import Any
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.models import ModelType
+from llama_stack.exceptions import UnsupportedModelError
 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 (
@@ -34,7 +35,9 @@ def get_huggingface_repo(model_descriptor: str) -> str | None:
 
 
 def build_hf_repo_model_entry(
-    provider_model_id: str, model_descriptor: str, additional_aliases: list[str] | None = None
+    provider_model_id: str,
+    model_descriptor: str,
+    additional_aliases: list[str] | None = None,
 ) -> ProviderModelEntry:
     aliases = [
         get_huggingface_repo(model_descriptor),
@@ -81,9 +84,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
 
     async def register_model(self, model: Model) -> Model:
         if not (supported_model_id := self.get_provider_model_id(model.provider_resource_id)):
-            raise ValueError(
+            raise UnsupportedModelError(model.provider_resource_id, self.alias_to_provider_id_map.keys())
-                f"Model '{model.provider_resource_id}' is not supported. Supported models are: {', '.join(self.alias_to_provider_id_map.keys())}"
-            )
         provider_resource_id = self.get_provider_model_id(model.model_id)
         if model.model_type == ModelType.embedding:
             # embedding models are always registered by their provider model id and does not need to be mapped to a llama model