fix(models): always prefix models with provider_id when registering

llama_stack/core/routing_tables/common.py

@@ -245,25 +245,7 @@ class CommonRoutingTableImpl(RoutingTable):
 
 
 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:
+    if not model:
-        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 ModelNotFoundError(model_id)
-
+    return model
-    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/core/routing_tables/models.py

@@ -33,7 +33,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
             try:
                 models = await provider.list_models()
             except Exception as e:
-                logger.debug(f"Model refresh failed for provider {provider_id}: {e}")
+                logger.warning(f"Model refresh failed for provider {provider_id}: {e}")
                 continue
 
             self.listed_providers.add(provider_id)
@@ -104,15 +104,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
         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
+        identifier = f"{provider_id}/{provider_model_id}"
-        # 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=identifier,
             provider_resource_id=provider_model_id,

llama_stack/core/store/registry.py

@@ -102,6 +102,9 @@ class DiskDistributionRegistry(DistributionRegistry):
                 "Unregister it first if you want to replace it."
             )
 
+        if "sentence-transformers/sentence-transformers" in obj.identifier:
+            raise Exception("OMG")
+
         await self.kvstore.set(
             KEY_FORMAT.format(type=obj.type, identifier=obj.identifier),
             obj.model_dump_json(),

scripts/integration-tests.sh

@@ -290,7 +290,7 @@ pytest -s -v $PYTEST_TARGET \
     -k "$PYTEST_PATTERN" \
     $EXTRA_PARAMS \
     --color=yes \
-    --embedding-model=nomic-ai/nomic-embed-text-v1.5 \
+    --embedding-model=sentence-transformers/nomic-ai/nomic-embed-text-v1.5 \
     --color=yes $EXTRA_PARAMS \
     --capture=tee-sys
 exit_code=$?

tests/integration/common/recordings/02c93bb3c314427bae2b7a7a6f054792b9f22d2cb4522eab802810be8672d3dc.json

@@ -12,26 +12,7 @@
     "body": {
       "__type__": "ollama._types.ProcessResponse",
       "__data__": {
-        "models": [
+        "models": []
-          {
-            "model": "llama-guard3:1b",
-            "name": "llama-guard3:1b",
-            "digest": "494147e06bf99e10dbe67b63a07ac81c162f18ef3341aa3390007ac828571b3b",
-            "expires_at": "2025-10-13T14:07:12.309717-07:00",
-            "size": 2279663616,
-            "size_vram": 2279663616,
-            "details": {
-              "parent_model": "",
-              "format": "gguf",
-              "family": "llama",
-              "families": [
-                "llama"
-              ],
-              "parameter_size": "1.5B",
-              "quantization_level": "Q8_0"
-            }
-          }
-        ]
       }
     },
     "is_streaming": false

tests/integration/fixtures/common.py

@@ -117,42 +117,24 @@ def client_with_models(
     text_model_id,
     vision_model_id,
     embedding_model_id,
-    embedding_dimension,
     judge_model_id,
 ):
     client = llama_stack_client
 
     providers = [p for p in client.providers.list() if p.api == "inference"]
     assert len(providers) > 0, "No inference providers found"
-    inference_providers = [p.provider_id for p in providers if p.provider_type != "inline::sentence-transformers"]
 
     model_ids = {m.identifier for m in client.models.list()}
-    model_ids.update(m.provider_resource_id for m in client.models.list())
 
-    # TODO: fix this crap where we use the first provider randomly
-    # that cannot be right. I think the test should just specify the provider_id
     if text_model_id and text_model_id not in model_ids:
-        client.models.register(model_id=text_model_id, provider_id=inference_providers[0])
+        raise ValueError(f"text_model_id {text_model_id} not found")
     if vision_model_id and vision_model_id not in model_ids:
-        client.models.register(model_id=vision_model_id, provider_id=inference_providers[0])
+        raise ValueError(f"vision_model_id {vision_model_id} not found")
     if judge_model_id and judge_model_id not in model_ids:
-        client.models.register(model_id=judge_model_id, provider_id=inference_providers[0])
+        raise ValueError(f"judge_model_id {judge_model_id} not found")
 
     if embedding_model_id and embedding_model_id not in model_ids:
-        # try to find a provider that supports embeddings, if sentence-transformers is not available
+        raise ValueError(f"embedding_model_id {embedding_model_id} not found")
-        selected_provider = None
-        for p in providers:
-            if p.provider_type == "inline::sentence-transformers":
-                selected_provider = p
-                break
-
-        selected_provider = selected_provider or providers[0]
-        client.models.register(
-            model_id=embedding_model_id,
-            provider_id=selected_provider.provider_id,
-            model_type="embedding",
-            metadata={"embedding_dimension": embedding_dimension or 768},
-        )
     return client