Merge branch 'main' into inference_refactor

llama_stack/distribution/library_client.py

@@ -257,6 +257,8 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         endpoints = get_all_api_endpoints()
         endpoint_impls = {}
         for api, api_endpoints in endpoints.items():
+            if api not in self.impls:
+                continue
             for endpoint in api_endpoints:
                 impl = self.impls[api]
                 func = getattr(impl, endpoint.name)

llama_stack/distribution/resolver.py

@@ -24,6 +24,7 @@ from llama_stack.apis.inspect import Inspect
 from llama_stack.apis.memory import Memory
 from llama_stack.apis.memory_banks import MemoryBanks
 from llama_stack.apis.models import Models
+from llama_stack.apis.post_training import PostTraining
 from llama_stack.apis.safety import Safety
 from llama_stack.apis.scoring import Scoring
 from llama_stack.apis.scoring_functions import ScoringFunctions
@@ -58,6 +59,7 @@ def api_protocol_map() -> Dict[Api, Any]:
         Api.scoring_functions: ScoringFunctions,
         Api.eval: Eval,
         Api.eval_tasks: EvalTasks,
+        Api.post_training: PostTraining,
     }
 
 

llama_stack/distribution/routers/routers.py

@@ -111,7 +111,7 @@ class InferenceRouter(Inference):
         model = await self.routing_table.get_model(model_id)
         if model is None:
             raise ValueError(f"Model '{model_id}' not found")
-        if model.model_type == ModelType.embedding_model:
+        if model.model_type == ModelType.embedding:
             raise ValueError(
                 f"Model '{model_id}' is an embedding model and does not support chat completions"
             )
@@ -144,7 +144,7 @@ class InferenceRouter(Inference):
         model = await self.routing_table.get_model(model_id)
         if model is None:
             raise ValueError(f"Model '{model_id}' not found")
-        if model.model_type == ModelType.embedding_model:
+        if model.model_type == ModelType.embedding:
             raise ValueError(
                 f"Model '{model_id}' is an embedding model and does not support chat completions"
             )

llama_stack/distribution/routers/routing_tables.py

@@ -233,10 +233,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
             metadata = {}
         if model_type is None:
             model_type = ModelType.llm
-        if (
-            "embedding_dimension" not in metadata
-            and model_type == ModelType.embedding_model
-        ):
+        if "embedding_dimension" not in metadata and model_type == ModelType.embedding:
             raise ValueError(
                 "Embedding model must have an embedding dimension in its metadata"
             )
@@ -323,8 +320,15 @@ class MemoryBanksRoutingTable(CommonRoutingTableImpl, MemoryBanks):
                 )
         model = await self.get_object_by_identifier("model", params.embedding_model)
         if model is None:
-            raise ValueError(f"Model {params.embedding_model} not found")
-        if model.model_type != ModelType.embedding_model:
+            if params.embedding_model == "all-MiniLM-L6-v2":
+                raise ValueError(
+                    "Embeddings are now served via Inference providers. "
+                    "Please upgrade your run.yaml to include inline::sentence-transformer as an additional inference provider. "
+                    "See https://github.com/meta-llama/llama-stack/blob/main/llama_stack/templates/together/run.yaml for an example."
+                )
+            else:
+                raise ValueError(f"Model {params.embedding_model} not found")
+        if model.model_type != ModelType.embedding:
             raise ValueError(
                 f"Model {params.embedding_model} is not an embedding model"
             )

llama_stack/distribution/tests/library_client_test.py

@@ -29,7 +29,8 @@ def main(config_path: str):
         print("No models found, skipping chat completion test")
         return
 
-    model_id = models[0].identifier
+    model_id = next(m.identifier for m in models if "8b" in m.identifier.lower())
+    print(f"Using model: {model_id}")
     response = client.inference.chat_completion(
         messages=[UserMessage(content="What is the capital of France?", role="user")],
         model_id=model_id,