Fix test infra, sentence embeddings mixin

llama_stack/distribution/library_client.py

@@ -230,12 +230,11 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         if Api.telemetry in self.impls:
             setup_logger(self.impls[Api.telemetry])
 
-        console = Console()
-        console.print(f"Using config [blue]{self.config_path_or_template_name}[/blue]:")
-
-        # Redact sensitive information before printing
-        safe_config = redact_sensitive_fields(self.config.model_dump())
-        console.print(yaml.dump(safe_config, indent=2))
+        if not os.environ.get("PYTEST_CURRENT_TEST"):
+            console = Console()
+            console.print(f"Using config [blue]{self.config_path_or_template_name}[/blue]:")
+            safe_config = redact_sensitive_fields(self.config.model_dump())
+            console.print(yaml.dump(safe_config, indent=2))
 
         endpoints = get_all_api_endpoints()
         endpoint_impls = {}

llama_stack/providers/utils/inference/embedding_mixin.py

@@ -14,6 +14,7 @@ from llama_stack.apis.inference import (
     ModelStore,
     TextTruncation,
 )
+from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
 
 EMBEDDING_MODELS = {}
 
@@ -34,7 +35,7 @@ class SentenceTransformerEmbeddingMixin:
     ) -> EmbeddingsResponse:
         model = await self.model_store.get_model(model_id)
         embedding_model = self._load_sentence_transformer_model(model.provider_resource_id)
-        embeddings = embedding_model.encode(contents)
+        embeddings = embedding_model.encode([interleaved_content_as_str(content) for content in contents])
         return EmbeddingsResponse(embeddings=embeddings)
 
     def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer":

tests/client-sdk/vector_io/conftest.py

@@ -1,22 +0,0 @@
-# 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.
-
-
-def pytest_addoption(parser):
-    parser.addoption(
-        "--embedding-model",
-        action="store",
-        default="all-MiniLM-L6-v2",
-        help="Specify the embedding model to use for testing",
-    )
-
-
-def pytest_generate_tests(metafunc):
-    if "embedding_model" in metafunc.fixturenames:
-        metafunc.parametrize(
-            "embedding_model",
-            [metafunc.config.getoption("--embedding-model")],
-        )

tests/client-sdk/vector_io/test_vector_io.py

@@ -36,12 +36,12 @@ def single_entry_vector_db_registry(llama_stack_client, empty_vector_db_registry
 
 
 @pytest.mark.parametrize("provider_id", INLINE_VECTOR_DB_PROVIDERS)
-def test_vector_db_retrieve(llama_stack_client, embedding_model, empty_vector_db_registry, provider_id):
+def test_vector_db_retrieve(llama_stack_client, embedding_model_id, empty_vector_db_registry, provider_id):
     # Register a memory bank first
     vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
     llama_stack_client.vector_dbs.register(
         vector_db_id=vector_db_id,
-        embedding_model=embedding_model,
+        embedding_model=embedding_model_id,
         embedding_dimension=384,
         provider_id=provider_id,
     )
@@ -50,7 +50,7 @@ def test_vector_db_retrieve(llama_stack_client, embedding_model, empty_vector_db
     response = llama_stack_client.vector_dbs.retrieve(vector_db_id=vector_db_id)
     assert response is not None
     assert response.identifier == vector_db_id
-    assert response.embedding_model == embedding_model
+    assert response.embedding_model == embedding_model_id
     assert response.provider_id == provider_id
     assert response.provider_resource_id == vector_db_id
 
@@ -61,11 +61,11 @@ def test_vector_db_list(llama_stack_client, empty_vector_db_registry):
 
 
 @pytest.mark.parametrize("provider_id", INLINE_VECTOR_DB_PROVIDERS)
-def test_vector_db_register(llama_stack_client, embedding_model, empty_vector_db_registry, provider_id):
+def test_vector_db_register(llama_stack_client, embedding_model_id, empty_vector_db_registry, provider_id):
     vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
     llama_stack_client.vector_dbs.register(
         vector_db_id=vector_db_id,
-        embedding_model=embedding_model,
+        embedding_model=embedding_model_id,
         embedding_dimension=384,
         provider_id=provider_id,
     )