chore: standardize model not found error (#2964)

# What does this PR do?
1. Creates a new `ModelNotFoundError` class
2. Implements the new class where appropriate 

Relates to #2379

Signed-off-by: Nathan Weinberg <nweinber@redhat.com>

llama_stack/apis/common/errors.py

@@ -11,3 +11,11 @@ class UnsupportedModelError(ValueError):
     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)
+
+
+class ModelNotFoundError(ValueError):
+    """raised when Llama Stack cannot find a referenced model"""
+
+    def __init__(self, model_name: str) -> None:
+        message = f"Model '{model_name}' not found. Use client.models.list() to list available models."
+        super().__init__(message)

llama_stack/distribution/routers/inference.py

@@ -17,6 +17,7 @@ from llama_stack.apis.common.content_types import (
     InterleavedContent,
     InterleavedContentItem,
 )
+from llama_stack.apis.common.errors import ModelNotFoundError
 from llama_stack.apis.inference import (
     BatchChatCompletionResponse,
     BatchCompletionResponse,
@@ -188,7 +189,7 @@ class InferenceRouter(Inference):
             sampling_params = SamplingParams()
         model = await self.routing_table.get_model(model_id)
         if model is None:
-            raise ValueError(f"Model '{model_id}' not found")
+            raise ModelNotFoundError(model_id)
         if model.model_type == ModelType.embedding:
             raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
         if tool_config:
@@ -317,7 +318,7 @@ class InferenceRouter(Inference):
         )
         model = await self.routing_table.get_model(model_id)
         if model is None:
-            raise ValueError(f"Model '{model_id}' not found")
+            raise ModelNotFoundError(model_id)
         if model.model_type == ModelType.embedding:
             raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
         provider = await self.routing_table.get_provider_impl(model_id)
@@ -390,7 +391,7 @@ class InferenceRouter(Inference):
         logger.debug(f"InferenceRouter.embeddings: {model_id}")
         model = await self.routing_table.get_model(model_id)
         if model is None:
-            raise ValueError(f"Model '{model_id}' not found")
+            raise ModelNotFoundError(model_id)
         if model.model_type == ModelType.llm:
             raise ValueError(f"Model '{model_id}' is an LLM model and does not support embeddings")
         provider = await self.routing_table.get_provider_impl(model_id)
@@ -430,7 +431,7 @@ class InferenceRouter(Inference):
         )
         model_obj = await self.routing_table.get_model(model)
         if model_obj is None:
-            raise ValueError(f"Model '{model}' not found")
+            raise ModelNotFoundError(model)
         if model_obj.model_type == ModelType.embedding:
             raise ValueError(f"Model '{model}' is an embedding model and does not support completions")
 
@@ -491,7 +492,7 @@ class InferenceRouter(Inference):
         )
         model_obj = await self.routing_table.get_model(model)
         if model_obj is None:
-            raise ValueError(f"Model '{model}' not found")
+            raise ModelNotFoundError(model)
         if model_obj.model_type == ModelType.embedding:
             raise ValueError(f"Model '{model}' is an embedding model and does not support chat completions")
 
@@ -562,7 +563,7 @@ class InferenceRouter(Inference):
         )
         model_obj = await self.routing_table.get_model(model)
         if model_obj is None:
-            raise ValueError(f"Model '{model}' not found")
+            raise ModelNotFoundError(model)
         if model_obj.model_type != ModelType.embedding:
             raise ValueError(f"Model '{model}' is not an embedding model")
 

llama_stack/distribution/routing_tables/common.py

@@ -6,6 +6,7 @@
 
 from typing import Any
 
+from llama_stack.apis.common.errors import ModelNotFoundError
 from llama_stack.apis.models import Model
 from llama_stack.apis.resource import ResourceType
 from llama_stack.apis.scoring_functions import ScoringFn
@@ -257,7 +258,7 @@ async def lookup_model(routing_table: CommonRoutingTableImpl, model_id: str) ->
     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")
+        raise ModelNotFoundError(model_id)
 
     if len(matching_models) > 1:
         raise ValueError(f"Multiple providers found for '{model_id}': {[m.provider_id for m in matching_models]}")

llama_stack/distribution/routing_tables/models.py

@@ -7,6 +7,7 @@
 import time
 from typing import Any
 
+from llama_stack.apis.common.errors import ModelNotFoundError
 from llama_stack.apis.models import ListModelsResponse, Model, Models, ModelType, OpenAIListModelsResponse, OpenAIModel
 from llama_stack.distribution.datatypes import (
     ModelWithOwner,
@@ -111,7 +112,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
     async def unregister_model(self, model_id: str) -> None:
         existing_model = await self.get_model(model_id)
         if existing_model is None:
-            raise ValueError(f"Model {model_id} not found")
+            raise ModelNotFoundError(model_id)
         await self.unregister_object(existing_model)
 
     async def update_registered_models(

llama_stack/distribution/routing_tables/vector_dbs.py

@@ -8,6 +8,7 @@ from typing import Any
 
 from pydantic import TypeAdapter
 
+from llama_stack.apis.common.errors import ModelNotFoundError
 from llama_stack.apis.models import ModelType
 from llama_stack.apis.resource import ResourceType
 from llama_stack.apis.vector_dbs import ListVectorDBsResponse, VectorDB, VectorDBs
@@ -63,7 +64,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
                 raise ValueError("No provider available. Please configure a vector_io provider.")
         model = await lookup_model(self, embedding_model)
         if model is None:
-            raise ValueError(f"Model {embedding_model} not found")
+            raise ModelNotFoundError(embedding_model)
         if model.model_type != ModelType.embedding:
             raise ValueError(f"Model {embedding_model} is not an embedding model")
         if "embedding_dimension" not in model.metadata: