Merge remote-tracking branch 'origin/main' into stores

tests/unit/core/routers/test_vector_io.py

@@ -0,0 +1,57 @@
+# 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.
+
+from unittest.mock import AsyncMock, Mock
+
+import pytest
+
+from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
+from llama_stack.core.routers.vector_io import VectorIORouter
+
+
+async def test_single_provider_auto_selection():
+    # provider_id automatically selected during vector store create() when only one provider available
+    mock_routing_table = Mock()
+    mock_routing_table.impls_by_provider_id = {"inline::faiss": "mock_provider"}
+    mock_routing_table.get_all_with_type = AsyncMock(
+        return_value=[
+            Mock(identifier="all-MiniLM-L6-v2", model_type="embedding", metadata={"embedding_dimension": 384})
+        ]
+    )
+    mock_routing_table.register_vector_db = AsyncMock(
+        return_value=Mock(identifier="vs_123", provider_id="inline::faiss", provider_resource_id="vs_123")
+    )
+    mock_routing_table.get_provider_impl = AsyncMock(
+        return_value=Mock(openai_create_vector_store=AsyncMock(return_value=Mock(id="vs_123")))
+    )
+    router = VectorIORouter(mock_routing_table)
+    request = OpenAICreateVectorStoreRequestWithExtraBody.model_validate(
+        {"name": "test_store", "embedding_model": "all-MiniLM-L6-v2"}
+    )
+
+    result = await router.openai_create_vector_store(request)
+    assert result.id == "vs_123"
+
+
+async def test_create_vector_stores_multiple_providers_missing_provider_id_error():
+    # if multiple providers are available, vector store create will error without provider_id
+    mock_routing_table = Mock()
+    mock_routing_table.impls_by_provider_id = {
+        "inline::faiss": "mock_provider_1",
+        "inline::sqlite-vec": "mock_provider_2",
+    }
+    mock_routing_table.get_all_with_type = AsyncMock(
+        return_value=[
+            Mock(identifier="all-MiniLM-L6-v2", model_type="embedding", metadata={"embedding_dimension": 384})
+        ]
+    )
+    router = VectorIORouter(mock_routing_table)
+    request = OpenAICreateVectorStoreRequestWithExtraBody.model_validate(
+        {"name": "test_store", "embedding_model": "all-MiniLM-L6-v2"}
+    )
+
+    with pytest.raises(ValueError, match="Multiple vector_io providers available"):
+        await router.openai_create_vector_store(request)