chore: import upstream snapshot with attribution

2026-07-13 13:21:23 +08:00
commit b957a53def
5423 changed files with 863745 additions and 0 deletions
@@ -0,0 +1,126 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+from typing import Any
+
+import pytest
+
+from semantic_kernel.connectors.ai.embedding_generator_base import EmbeddingGeneratorBase
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+from tests.integration.embeddings.test_embedding_service_base import (
+    EmbeddingServiceTestBase,
+    google_ai_setup,
+    mistral_ai_setup,
+    ollama_setup,
+    vertex_ai_setup,
+)
+
+pytestmark = pytest.mark.parametrize(
+    "service_id, execution_settings_kwargs, output_dimensionality",
+    [
+        pytest.param(
+            "openai",
+            {},
+            1536,  # text-embedding-ada-002 doesn't support custom output dimensionality
+            id="openai",
+        ),
+        pytest.param(
+            "azure",
+            {},
+            1536,  # text-embedding-ada-002 doesn't support custom output dimensionality
+            id="azure",
+        ),
+        pytest.param(
+            "azure_custom_client",
+            {},
+            1536,  # text-embedding-ada-002 doesn't support custom output dimensionality
+            id="azure_custom_client",
+        ),
+        pytest.param(
+            "azure_ai_inference",
+            {},
+            1536,  # text-embedding-ada-002 doesn't support custom output dimensionality
+            id="azure_ai_inference",
+        ),
+        pytest.param(
+            "mistral_ai",
+            {},
+            1024,
+            marks=pytest.mark.skipif(not mistral_ai_setup, reason="Mistral AI environment variables not set"),
+            id="mistral_ai",
+        ),
+        pytest.param(
+            "hugging_face",
+            {},
+            384,
+            id="hugging_face",
+        ),
+        pytest.param(
+            "ollama",
+            {},
+            768,
+            marks=(
+                pytest.mark.skipif(not ollama_setup, reason="Ollama not setup"),
+                pytest.mark.ollama,
+            ),
+            id="ollama",
+        ),
+        pytest.param(
+            "google_ai",
+            {"output_dimensionality": 10},
+            10,
+            marks=pytest.mark.skipif(not google_ai_setup, reason="Google AI environment variables not set"),
+            id="google_ai",
+        ),
+        pytest.param(
+            "vertex_ai",
+            {"output_dimensionality": 10},
+            10,
+            marks=(
+                pytest.mark.skipif(not vertex_ai_setup, reason="Vertex AI environment variables not set"),
+                pytest.mark.timeout(300),  # Vertex AI may take longer time
+            ),
+            id="vertex_ai",
+        ),
+        pytest.param(
+            "bedrock_amazon_titan-v1",
+            {},
+            1536,  # This model doesn't support custom output dimensionality
+            id="bedrock_amazon_titan-v1",
+        ),
+        pytest.param(
+            "bedrock_amazon_titan-v2",
+            {"extension_data": {"dimensions": 256}},
+            256,
+            id="bedrock_amazon_titan-v2",
+        ),
+        pytest.param(
+            "bedrock_cohere",
+            {},
+            1024,
+            id="bedrock_cohere",
+        ),
+    ],
+)
+
+
+class TestEmbeddingService(EmbeddingServiceTestBase):
+    """Test embedding service with memory.
+
+    This tests if the embedding service can be used with the semantic memory.
+    """
+
+    async def test_embedding_service(
+        self,
+        service_id,
+        services: dict[str, tuple[EmbeddingGeneratorBase, type[PromptExecutionSettings]]],
+        execution_settings_kwargs: dict[str, Any],
+        output_dimensionality: int,
+    ):
+        embedding_generator, settings_type = services[service_id]
+        embeddings = await embedding_generator.generate_embeddings(
+            texts=["Hello, world!", "Hello, universe!"],
+            settings=settings_type(**execution_settings_kwargs),
+        )
+
+        assert embeddings.shape == (2, output_dimensionality)