remove hardcoded all-mini

llama_stack/providers/inline/agents/meta_reference/__init__.py

@@ -21,6 +21,7 @@ async def get_provider_impl(config: MetaReferenceAgentsImplConfig, deps: Dict[Ap
         deps[Api.safety],
         deps[Api.tool_runtime],
         deps[Api.tool_groups],
+        deps[Api.models],
     )
     await impl.initialize()
     return impl

llama_stack/providers/inline/agents/meta_reference/agent_instance.py

@@ -59,6 +59,7 @@ from llama_stack.apis.inference import (
     ToolResponseMessage,
     UserMessage,
 )
+from llama_stack.apis.models import Models
 from llama_stack.apis.safety import Safety
 from llama_stack.apis.tools import RAGDocument, RAGQueryConfig, ToolGroups, ToolRuntime
 from llama_stack.apis.vector_io import VectorIO
@@ -94,6 +95,7 @@ class ChatAgent(ShieldRunnerMixin):
         tool_runtime_api: ToolRuntime,
         tool_groups_api: ToolGroups,
         vector_io_api: VectorIO,
+        models_api: Models,
         persistence_store: KVStore,
     ):
         self.agent_id = agent_id
@@ -102,6 +104,7 @@ class ChatAgent(ShieldRunnerMixin):
         self.inference_api = inference_api
         self.safety_api = safety_api
         self.vector_io_api = vector_io_api
+        self.models_api = models_api
         self.storage = AgentPersistence(agent_id, persistence_store)
         self.tool_runtime_api = tool_runtime_api
         self.tool_groups_api = tool_groups_api
@@ -825,9 +828,12 @@ class ChatAgent(ShieldRunnerMixin):
             # TODO: the semantic for registration is definitely not "creation"
             # so we need to fix it if we expect the agent to create a new vector db
             # for each session
+            list_models_response = await self.models_api.list_models()
+            embdding_models = [x for x in list_models_response.data if x.model_type == "embedding"]
             await self.vector_io_api.register_vector_db(
                 vector_db_id=vector_db_id,
-                embedding_model="all-MiniLM-L6-v2",
+                embedding_model=embdding_models[0].identifier,
+                embedding_dimension=embdding_models[0].metadata["embedding_dimension"],
             )
             await self.storage.add_vector_db_to_session(session_id, vector_db_id)
         else:

llama_stack/providers/inline/agents/meta_reference/agents.py

@@ -31,6 +31,7 @@ from llama_stack.apis.inference import (
     ToolResponseMessage,
     UserMessage,
 )
+from llama_stack.apis.models import Models
 from llama_stack.apis.safety import Safety
 from llama_stack.apis.tools import ToolGroups, ToolRuntime
 from llama_stack.apis.vector_io import VectorIO
@@ -52,6 +53,7 @@ class MetaReferenceAgentsImpl(Agents):
         safety_api: Safety,
         tool_runtime_api: ToolRuntime,
         tool_groups_api: ToolGroups,
+        models_api: Models,
     ):
         self.config = config
         self.inference_api = inference_api
@@ -59,6 +61,7 @@ class MetaReferenceAgentsImpl(Agents):
         self.safety_api = safety_api
         self.tool_runtime_api = tool_runtime_api
         self.tool_groups_api = tool_groups_api
+        self.models_api = models_api
 
         self.in_memory_store = InmemoryKVStoreImpl()
         self.tempdir = tempfile.mkdtemp()
@@ -115,6 +118,7 @@ class MetaReferenceAgentsImpl(Agents):
             vector_io_api=self.vector_io_api,
             tool_runtime_api=self.tool_runtime_api,
             tool_groups_api=self.tool_groups_api,
+            models_api=self.models_api,
             persistence_store=(
                 self.persistence_store if agent_config.enable_session_persistence else self.in_memory_store
             ),

llama_stack/providers/registry/agents.py

@@ -37,6 +37,7 @@ def available_providers() -> List[ProviderSpec]:
                 Api.vector_dbs,
                 Api.tool_runtime,
                 Api.tool_groups,
+                Api.models,
             ],
         ),
         remote_provider_spec(