fix in filter and 100% test coverage

python/semantic_kernel/connectors/memory/qdrant/qdrant_collection.py

@@ -188,7 +188,7 @@ async def _inner_search(
             else:
                 query_vector = vector
         if query_vector is None:
-            raise VectorSearchExecutionException("Search requires either a vector.")
+            raise VectorSearchExecutionException("Search requires a vector.")
         results = await self.qdrant_client.search(
             collection_name=self.collection_name,
             query_vector=query_vector,
@@ -214,7 +214,7 @@ def _get_score_from_result(self, result: ScoredPoint) -> float:
     def _create_filter(self, options: VectorSearchOptions) -> Filter:
         return Filter(
             must=[
-                FieldCondition(key=filter.field_name, match=MatchAny(any=filter.value))
+                FieldCondition(key=filter.field_name, match=MatchAny(any=[filter.value]))
                 for filter in options.filter.filters
             ]
         )

python/tests/unit/connectors/memory/qdrant/test_qdrant.py

@@ -4,17 +4,19 @@
 
 from pytest import fixture, mark, raises
 from qdrant_client.async_qdrant_client import AsyncQdrantClient
-from qdrant_client.models import Datatype, Distance, VectorParams
+from qdrant_client.models import Datatype, Distance, FieldCondition, Filter, MatchAny, VectorParams
 
 from semantic_kernel.connectors.memory.qdrant.qdrant_collection import QdrantCollection
 from semantic_kernel.connectors.memory.qdrant.qdrant_store import QdrantStore
 from semantic_kernel.data.record_definition.vector_store_record_fields import VectorStoreRecordVectorField
+from semantic_kernel.data.vector_search.vector_search_filter import VectorSearchFilter
 from semantic_kernel.data.vector_search.vector_search_options import VectorSearchOptions
 from semantic_kernel.exceptions.memory_connector_exceptions import (
     MemoryConnectorException,
     MemoryConnectorInitializationError,
     VectorStoreModelValidationError,
 )
+from semantic_kernel.exceptions.search_exceptions import VectorSearchExecutionException
 
 BASE_PATH = "qdrant_client.async_qdrant_client.AsyncQdrantClient"
 
@@ -119,9 +121,10 @@ def mock_search():
         yield mock_search
 
 
-def test_vector_store_defaults(vector_store):
-    assert vector_store.qdrant_client is not None
-    assert vector_store.qdrant_client._client.rest_uri == "http://localhost:6333"
+async def test_vector_store_defaults(vector_store):
+    async with vector_store:
+        assert vector_store.qdrant_client is not None
+        assert vector_store.qdrant_client._client.rest_uri == "http://localhost:6333"
 
 
 def test_vector_store_with_client():
@@ -162,18 +165,18 @@ def test_get_collection(vector_store, data_model_definition, qdrant_unit_test_en
     assert vector_store.vector_record_collections["test"] == collection
 
 
-def test_collection_init(data_model_definition, qdrant_unit_test_env):
-    collection = QdrantCollection(
+async def test_collection_init(data_model_definition, qdrant_unit_test_env):
+    async with QdrantCollection(
         data_model_type=dict,
         collection_name="test",
         data_model_definition=data_model_definition,
         env_file_path="test.env",
-    )
-    assert collection.collection_name == "test"
-    assert collection.qdrant_client is not None
-    assert collection.data_model_type is dict
-    assert collection.data_model_definition == data_model_definition
-    assert collection.named_vectors
+    ) as collection:
+        assert collection.collection_name == "test"
+        assert collection.qdrant_client is not None
+        assert collection.data_model_type is dict
+        assert collection.data_model_definition == data_model_definition
+        assert collection.named_vectors
 
 
 def test_collection_init_fail(data_model_definition):
@@ -275,8 +278,63 @@ async def test_create_index_fail(collection_to_use, request):
         await collection.create_collection()
 
 
-async def test_search(collection):
+async def test_search(collection, mock_search):
     results = await collection._inner_search(vector=[1.0, 2.0, 3.0], options=VectorSearchOptions(include_vectors=False))
     async for result in results.results:
         assert result.record["id"] == "id1"
         break
+
+    assert mock_search.call_count == 1
+    mock_search.assert_called_with(
+        collection_name="test",
+        query_vector=[1.0, 2.0, 3.0],
+        query_filter=Filter(must=[]),
+        with_vectors=False,
+        limit=3,
+        offset=0,
+    )
+
+
+async def test_search_named_vectors(collection, mock_search):
+    collection.named_vectors = True
+    results = await collection._inner_search(
+        vector=[1.0, 2.0, 3.0], options=VectorSearchOptions(vector_field_name="vector", include_vectors=False)
+    )
+    async for result in results.results:
+        assert result.record["id"] == "id1"
+        break
+
+    assert mock_search.call_count == 1
+    mock_search.assert_called_with(
+        collection_name="test",
+        query_vector=("vector", [1.0, 2.0, 3.0]),
+        query_filter=Filter(must=[]),
+        with_vectors=False,
+        limit=3,
+        offset=0,
+    )
+
+
+async def test_search_filter(collection, mock_search):
+    results = await collection._inner_search(
+        vector=[1.0, 2.0, 3.0],
+        options=VectorSearchOptions(include_vectors=False, filter=VectorSearchFilter.equal_to("id", "id1")),
+    )
+    async for result in results.results:
+        assert result.record["id"] == "id1"
+        break
+
+    assert mock_search.call_count == 1
+    mock_search.assert_called_with(
+        collection_name="test",
+        query_vector=[1.0, 2.0, 3.0],
+        query_filter=Filter(must=[FieldCondition(key="id", match=MatchAny(any=["id1"]))]),
+        with_vectors=False,
+        limit=3,
+        offset=0,
+    )
+
+
+async def test_search_fail(collection):
+    with raises(VectorSearchExecutionException, match="Search requires a vector."):
+        await collection._inner_search(options=VectorSearchOptions(include_vectors=False))