Vector Store Integration Tests

.semversioner/next-release/patch-20250331184323312702.json

@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "add vector store integration tests"
+}

graphrag/vector_stores/cosmosdb.py

@@ -7,6 +7,7 @@
 from typing import Any
 
 from azure.cosmos import ContainerProxy, CosmosClient, DatabaseProxy
+from azure.cosmos.exceptions import CosmosHttpResponseError
 from azure.cosmos.partition_key import PartitionKey
 from azure.identity import DefaultAzureCredential
 
@@ -19,7 +20,7 @@
 )
 
 
-class CosmosDBVectoreStore(BaseVectorStore):
+class CosmosDBVectorStore(BaseVectorStore):
     """Azure CosmosDB vector storage implementation."""
 
     _cosmos_client: CosmosClient
@@ -99,16 +100,32 @@ def _create_container(self) -> None:
             "automatic": True,
             "includedPaths": [{"path": "/*"}],
             "excludedPaths": [{"path": "/_etag/?"}, {"path": "/vector/*"}],
-            "vectorIndexes": [{"path": "/vector", "type": "diskANN"}],
         }
 
-        # Create the container and container client
-        self._database_client.create_container_if_not_exists(
-            id=self._container_name,
-            partition_key=partition_key,
-            indexing_policy=indexing_policy,
-            vector_embedding_policy=vector_embedding_policy,
-        )
+        # Currently, the CosmosDB emulator does not support the diskANN policy.
+        try:
+            # First try with the standard diskANN policy
+            indexing_policy["vectorIndexes"] = [{"path": "/vector", "type": "diskANN"}]
+
+            # Create the container and container client
+            self._database_client.create_container_if_not_exists(
+                id=self._container_name,
+                partition_key=partition_key,
+                indexing_policy=indexing_policy,
+                vector_embedding_policy=vector_embedding_policy,
+            )
+        except CosmosHttpResponseError:
+            # If diskANN fails (likely in emulator), retry without vector indexes
+            indexing_policy.pop("vectorIndexes", None)
+
+            # Create the container with compatible indexing policy
+            self._database_client.create_container_if_not_exists(
+                id=self._container_name,
+                partition_key=partition_key,
+                indexing_policy=indexing_policy,
+                vector_embedding_policy=vector_embedding_policy,
+            )
+
         self._container_client = self._database_client.get_container_client(
             self._container_name
         )
@@ -157,13 +174,46 @@ def similarity_search_by_vector(
             msg = "Container client is not initialized."
             raise ValueError(msg)
 
-        query = f"SELECT TOP {k} c.id, c.text, c.vector, c.attributes, VectorDistance(c.vector, @embedding) AS SimilarityScore FROM c ORDER BY VectorDistance(c.vector, @embedding)"  # noqa: S608
-        query_params = [{"name": "@embedding", "value": query_embedding}]
-        items = self._container_client.query_items(
-            query=query,
-            parameters=query_params,
-            enable_cross_partition_query=True,
-        )
+        try:
+            query = f"SELECT TOP {k} c.id, c.text, c.vector, c.attributes, VectorDistance(c.vector, @embedding) AS SimilarityScore FROM c ORDER BY VectorDistance(c.vector, @embedding)"  # noqa: S608
+            query_params = [{"name": "@embedding", "value": query_embedding}]
+            items = list(
+                self._container_client.query_items(
+                    query=query,
+                    parameters=query_params,
+                    enable_cross_partition_query=True,
+                )
+            )
+        except (CosmosHttpResponseError, ValueError):
+            # Currently, the CosmosDB emulator does not support the VectorDistance function.
+            # For emulator or test environments - fetch all items and calculate distance locally
+            query = "SELECT c.id, c.text, c.vector, c.attributes FROM c"
+            items = list(
+                self._container_client.query_items(
+                    query=query,
+                    enable_cross_partition_query=True,
+                )
+            )
+
+            # Calculate cosine similarity locally (1 - cosine distance)
+            from numpy import dot
+            from numpy.linalg import norm
+
+            def cosine_similarity(a, b):
+                if norm(a) * norm(b) == 0:
+                    return 0.0
+                return dot(a, b) / (norm(a) * norm(b))
+
+            # Calculate scores for all items
+            for item in items:
+                item_vector = item.get("vector", [])
+                similarity = cosine_similarity(query_embedding, item_vector)
+                item["SimilarityScore"] = similarity
+
+            # Sort by similarity score (higher is better) and take top k
+            items = sorted(
+                items, key=lambda x: x.get("SimilarityScore", 0.0), reverse=True
+            )[:k]
 
         return [
             VectorStoreSearchResult(
@@ -214,3 +264,8 @@ def search_by_id(self, id: str) -> VectorStoreDocument:
             text=item.get("text", ""),
             attributes=(json.loads(item.get("attributes", "{}"))),
         )
+
+    def clear(self) -> None:
+        """Clear the vector store."""
+        self._delete_container()
+        self._delete_database()

graphrag/vector_stores/factory.py

@@ -8,7 +8,7 @@
 
 from graphrag.vector_stores.azure_ai_search import AzureAISearchVectorStore
 from graphrag.vector_stores.base import BaseVectorStore
-from graphrag.vector_stores.cosmosdb import CosmosDBVectoreStore
+from graphrag.vector_stores.cosmosdb import CosmosDBVectorStore
 from graphrag.vector_stores.lancedb import LanceDBVectorStore
 
 
@@ -44,7 +44,7 @@ def create_vector_store(
             case VectorStoreType.AzureAISearch:
                 return AzureAISearchVectorStore(**kwargs)
             case VectorStoreType.CosmosDB:
-                return CosmosDBVectoreStore(**kwargs)
+                return CosmosDBVectorStore(**kwargs)
             case _:
                 if vector_store_type in cls.vector_store_types:
                     return cls.vector_store_types[vector_store_type](**kwargs)

tests/integration/vector_stores/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Integration tests for vector store implementations."""

tests/integration/vector_stores/test_azure_ai_search.py

@@ -0,0 +1,146 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Integration tests for Azure AI Search vector store implementation."""
+
+import os
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from graphrag.vector_stores.azure_ai_search import AzureAISearchVectorStore
+from graphrag.vector_stores.base import VectorStoreDocument
+
+TEST_AZURE_AI_SEARCH_URL = os.environ.get(
+    "TEST_AZURE_AI_SEARCH_URL", "https://test-url.search.windows.net"
+)
+TEST_AZURE_AI_SEARCH_KEY = os.environ.get("TEST_AZURE_AI_SEARCH_KEY", "test_api_key")
+
+
+class TestAzureAISearchVectorStore:
+    """Test class for AzureAISearchVectorStore."""
+
+    @pytest.fixture
+    def mock_search_client(self):
+        """Create a mock Azure AI Search client."""
+        with patch(
+            "graphrag.vector_stores.azure_ai_search.SearchClient"
+        ) as mock_client:
+            yield mock_client.return_value
+
+    @pytest.fixture
+    def mock_index_client(self):
+        """Create a mock Azure AI Search index client."""
+        with patch(
+            "graphrag.vector_stores.azure_ai_search.SearchIndexClient"
+        ) as mock_client:
+            yield mock_client.return_value
+
+    @pytest.fixture
+    def vector_store(self, mock_search_client, mock_index_client):
+        """Create an Azure AI Search vector store instance."""
+        vector_store = AzureAISearchVectorStore(collection_name="test_vectors")
+
+        # Create the necessary mocks first
+        vector_store.db_connection = mock_search_client
+        vector_store.index_client = mock_index_client
+
+        vector_store.connect(
+            url=TEST_AZURE_AI_SEARCH_URL,
+            api_key=TEST_AZURE_AI_SEARCH_KEY,
+            vector_size=5,
+        )
+        return vector_store
+
+    @pytest.fixture
+    def sample_documents(self):
+        """Create sample documents for testing."""
+        return [
+            VectorStoreDocument(
+                id="doc1",
+                text="This is document 1",
+                vector=[0.1, 0.2, 0.3, 0.4, 0.5],
+                attributes={"title": "Doc 1", "category": "test"},
+            ),
+            VectorStoreDocument(
+                id="doc2",
+                text="This is document 2",
+                vector=[0.2, 0.3, 0.4, 0.5, 0.6],
+                attributes={"title": "Doc 2", "category": "test"},
+            ),
+        ]
+
+    async def test_vector_store_operations(
+        self, vector_store, sample_documents, mock_search_client, mock_index_client
+    ):
+        """Test basic vector store operations with Azure AI Search."""
+        # Setup mock responses
+        mock_index_client.list_index_names.return_value = []
+        mock_index_client.create_or_update_index = MagicMock()
+        mock_search_client.upload_documents = MagicMock()
+
+        search_results = [
+            {
+                "id": "doc1",
+                "text": "This is document 1",
+                "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
+                "attributes": '{"title": "Doc 1", "category": "test"}',
+                "@search.score": 0.9,
+            },
+            {
+                "id": "doc2",
+                "text": "This is document 2",
+                "vector": [0.2, 0.3, 0.4, 0.5, 0.6],
+                "attributes": '{"title": "Doc 2", "category": "test"}',
+                "@search.score": 0.8,
+            },
+        ]
+        mock_search_client.search.return_value = search_results
+
+        mock_search_client.get_document.return_value = {
+            "id": "doc1",
+            "text": "This is document 1",
+            "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
+            "attributes": '{"title": "Doc 1", "category": "test"}',
+        }
+
+        vector_store.load_documents(sample_documents)
+        assert mock_index_client.create_or_update_index.called
+        assert mock_search_client.upload_documents.called
+
+        filter_query = vector_store.filter_by_id(["doc1", "doc2"])
+        assert filter_query == "search.in(id, 'doc1,doc2', ',')"
+
+        vector_results = vector_store.similarity_search_by_vector(
+            [0.1, 0.2, 0.3, 0.4, 0.5], k=2
+        )
+        assert len(vector_results) == 2
+        assert vector_results[0].document.id == "doc1"
+        assert vector_results[0].score == 0.9
+
+        # Define a simple text embedder function for testing
+        def mock_embedder(text: str) -> list[float]:
+            return [0.1, 0.2, 0.3, 0.4, 0.5]
+
+        text_results = vector_store.similarity_search_by_text(
+            "test query", mock_embedder, k=2
+        )
+        assert len(text_results) == 2
+
+        doc = vector_store.search_by_id("doc1")
+        assert doc.id == "doc1"
+        assert doc.text == "This is document 1"
+        assert doc.attributes["title"] == "Doc 1"
+
+    async def test_empty_embedding(self, vector_store, mock_search_client):
+        """Test similarity search by text with empty embedding."""
+
+        # Create a mock embedder that returns None and verify that no results are produced
+        def none_embedder(text: str) -> None:
+            return None
+
+        results = vector_store.similarity_search_by_text(
+            "test query", none_embedder, k=1
+        )
+        assert not mock_search_client.search.called
+        assert len(results) == 0