update resolver to only pass vector_stores section of run config

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

Using Router only from VectorDBs

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

removing model_api from vector store providers

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

fix test

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

updating integration tests

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

special handling for replay mode for available providers

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

tests/integration/fixtures/common.py

@@ -241,7 +241,7 @@ def instantiate_llama_stack_client(session):
         # --stack-config bypasses template so need this to set default embedding model
         if "vector_io" in config and "inference" in config:
             run_config.vector_stores = VectorStoresConfig(
-                default_embedding_model_id="inline::sentence-transformers/nomic-ai/nomic-embed-text-v1.5"
+                default_embedding_model_id="sentence-transformers/nomic-ai/nomic-embed-text-v1.5"
             )
 
         run_config_file = tempfile.NamedTemporaryFile(delete=False, suffix=".yaml")

tests/unit/distribution/test_single_provider_filter.py

@@ -0,0 +1,88 @@
+# 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.
+
+import pytest
+
+from llama_stack.cli.stack._build import _apply_single_provider_filter
+from llama_stack.core.datatypes import BuildConfig, BuildProvider, DistributionSpec
+from llama_stack.core.utils.image_types import LlamaStackImageType
+
+
+def test_filters_single_api():
+    """Test filtering keeps only specified provider for one API."""
+    build_config = BuildConfig(
+        image_type=LlamaStackImageType.VENV.value,
+        distribution_spec=DistributionSpec(
+            providers={
+                "vector_io": [
+                    BuildProvider(provider_type="inline::faiss"),
+                    BuildProvider(provider_type="inline::sqlite-vec"),
+                ],
+                "inference": [
+                    BuildProvider(provider_type="remote::openai"),
+                ],
+            },
+            description="Test",
+        ),
+    )
+
+    filtered = _apply_single_provider_filter(build_config, "vector_io=inline::sqlite-vec")
+
+    assert len(filtered.distribution_spec.providers["vector_io"]) == 1
+    assert filtered.distribution_spec.providers["vector_io"][0].provider_type == "inline::sqlite-vec"
+    assert len(filtered.distribution_spec.providers["inference"]) == 1  # unchanged
+
+
+def test_filters_multiple_apis():
+    """Test filtering multiple APIs."""
+    build_config = BuildConfig(
+        image_type=LlamaStackImageType.VENV.value,
+        distribution_spec=DistributionSpec(
+            providers={
+                "vector_io": [
+                    BuildProvider(provider_type="inline::faiss"),
+                    BuildProvider(provider_type="inline::sqlite-vec"),
+                ],
+                "inference": [
+                    BuildProvider(provider_type="remote::openai"),
+                    BuildProvider(provider_type="remote::anthropic"),
+                ],
+            },
+            description="Test",
+        ),
+    )
+
+    filtered = _apply_single_provider_filter(build_config, "vector_io=inline::faiss,inference=remote::openai")
+
+    assert len(filtered.distribution_spec.providers["vector_io"]) == 1
+    assert filtered.distribution_spec.providers["vector_io"][0].provider_type == "inline::faiss"
+    assert len(filtered.distribution_spec.providers["inference"]) == 1
+    assert filtered.distribution_spec.providers["inference"][0].provider_type == "remote::openai"
+
+
+def test_provider_not_found_exits():
+    """Test error when specified provider doesn't exist."""
+    build_config = BuildConfig(
+        image_type=LlamaStackImageType.VENV.value,
+        distribution_spec=DistributionSpec(
+            providers={"vector_io": [BuildProvider(provider_type="inline::faiss")]},
+            description="Test",
+        ),
+    )
+
+    with pytest.raises(SystemExit):
+        _apply_single_provider_filter(build_config, "vector_io=inline::nonexistent")
+
+
+def test_invalid_format_exits():
+    """Test error for invalid filter format."""
+    build_config = BuildConfig(
+        image_type=LlamaStackImageType.VENV.value,
+        distribution_spec=DistributionSpec(providers={}, description="Test"),
+    )
+
+    with pytest.raises(SystemExit):
+        _apply_single_provider_filter(build_config, "invalid_format")

tests/unit/providers/vector_io/conftest.py

@@ -144,7 +144,6 @@ async def sqlite_vec_adapter(sqlite_vec_db_path, unique_kvstore_config, mock_inf
         config=config,
         inference_api=mock_inference_api,
         files_api=None,
-        models_api=None,
     )
     collection_id = f"sqlite_test_collection_{np.random.randint(1e6)}"
     await adapter.initialize()
@@ -183,7 +182,6 @@ async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding
         config=config,
         inference_api=mock_inference_api,
         files_api=None,
-        models_api=None,
     )
     await adapter.initialize()
     await adapter.register_vector_db(

tests/unit/providers/vector_io/test_faiss.py

@@ -11,7 +11,6 @@ import numpy as np
 import pytest
 
 from llama_stack.apis.files import Files
-from llama_stack.apis.models import Models
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
 from llama_stack.providers.datatypes import HealthStatus
@@ -76,12 +75,6 @@ def mock_files_api():
     return mock_api
 
 
-@pytest.fixture
-def mock_models_api():
-    mock_api = MagicMock(spec=Models)
-    return mock_api
-
-
 @pytest.fixture
 def faiss_config():
     config = MagicMock(spec=FaissVectorIOConfig)
@@ -117,7 +110,7 @@ async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_
         assert response.chunks[1] == sample_chunks[1]
 
 
-async def test_health_success(mock_models_api):
+async def test_health_success():
     """Test that the health check returns OK status when faiss is working correctly."""
     # Create a fresh instance of FaissVectorIOAdapter for testing
     config = MagicMock()
@@ -126,9 +119,7 @@ async def test_health_success(mock_models_api):
 
     with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
         mock_index_flat.return_value = MagicMock()
-        adapter = FaissVectorIOAdapter(
-            config=config, inference_api=inference_api, models_api=mock_models_api, files_api=files_api
-        )
+        adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
 
         # Calling the health method directly
         response = await adapter.health()
@@ -142,7 +133,7 @@ async def test_health_success(mock_models_api):
         mock_index_flat.assert_called_once_with(128)  # VECTOR_DIMENSION is 128
 
 
-async def test_health_failure(mock_models_api):
+async def test_health_failure():
     """Test that the health check returns ERROR status when faiss encounters an error."""
     # Create a fresh instance of FaissVectorIOAdapter for testing
     config = MagicMock()
@@ -152,9 +143,7 @@ async def test_health_failure(mock_models_api):
     with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
         mock_index_flat.side_effect = Exception("Test error")
 
-        adapter = FaissVectorIOAdapter(
-            config=config, inference_api=inference_api, models_api=mock_models_api, files_api=files_api
-        )
+        adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
 
         # Calling the health method directly
         response = await adapter.health()

tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py

@@ -1162,5 +1162,5 @@ async def test_embedding_config_required_model_missing(vector_io_adapter):
     # Test with no embedding model provided
     params = OpenAICreateVectorStoreRequestWithExtraBody(name="test_store", metadata={})
 
-    with pytest.raises(ValueError, match="embedding_model is required in extra_body when creating a vector store"):
+    with pytest.raises(ValueError, match="embedding_model is required"):
         await vector_io_adapter.openai_create_vector_store(params)