Eduard van Valkenburg (eavanvalkenburg) · Oct 26, 2026 · Oct 7, 2026 · Oct 8, 2026
@@ -136,7 +136,7 @@ postgres = [
     "psycopg[binary,pool] ~= 3.2"
 ]
 qdrant = [
-    "qdrant-client ~= 1.9"
+    "qdrant-client ~= 1.10"
 ]
 redis = [
     "redis[hiredis] >= 6,< 8",

@@ -274,8 +274,6 @@ async def _inner_search(
         vector: Sequence[float | int] | None = None,
         **kwargs: Any,
     ) -> KernelSearchResults[VectorSearchResult[TModel]]:
-        query_vector: tuple[str, Sequence[float | int]] | Sequence[float | int] | None = None
-
         if not vector:
             vector = await self._generate_vector_from_values(values, options)
 
@@ -287,19 +285,24 @@ async def _inner_search(
             raise VectorStoreOperationException(
                 f"Vector field {options.vector_property_name} not found in data model definition."
             )
-        query_vector = (vector_field.storage_name or vector_field.name, vector) if self.named_vectors else vector
         filters: Filter | list[Filter] | None = self._build_filter(options.filter)  # type: ignore
-        filter: Filter | None = Filter(must=filters) if filters and isinstance(filters, list) else filters  # type: ignore
+        filter: Filter | None = (
+            Filter(must=filters if isinstance(filters, list) else [filters])  # type: ignore
+            if filters and not isinstance(filters, Filter)
+            else filters
+        )
         if search_type == SearchType.VECTOR:
-            results = await self.qdrant_client.search(
+            points = await self.qdrant_client.query_points(
                 collection_name=self.collection_name,
-                query_vector=query_vector,  # type: ignore
+                query=vector,  # type: ignore
+                using=(vector_field.storage_name or vector_field.name) if self.named_vectors else None,
                 query_filter=filter,
                 with_vectors=options.include_vectors,
                 limit=options.top,
                 offset=options.skip,
                 **kwargs,
             )
+            results = points.points
         else:
             # Hybrid search: vector + keywords (RRF fusion)
             # 1. Get keywords and text field

@@ -4,7 +4,7 @@
 
 from pytest import fixture, mark, raises
 from qdrant_client.async_qdrant_client import AsyncQdrantClient
-from qdrant_client.models import Datatype, Distance, FieldCondition, MatchValue, VectorParams
+from qdrant_client.models import Datatype, Distance, FieldCondition, Filter, MatchValue, VectorParams
 
 from semantic_kernel.connectors.qdrant import QdrantCollection, QdrantStore
 from semantic_kernel.data.vector import DistanceFunction, VectorStoreField
@@ -109,12 +109,12 @@ def mock_delete():
 
 @fixture(autouse=True)
 def mock_search():
-    with patch(f"{BASE_PATH}.search") as mock_search:
+    with patch(f"{BASE_PATH}.query_points") as mock_search:
         from qdrant_client.models import ScoredPoint
 
         response1 = ScoredPoint(id="id1", version=1, score=0.0, payload={"content": "content"})
         response2 = ScoredPoint(id="id2", version=1, score=0.0, payload={"content": "content"})
-        mock_search.return_value = [response1, response2]
+        mock_search.return_value = MagicMock(points=[response1, response2])
         yield mock_search
 
 
@@ -285,7 +285,8 @@ async def test_search(collection, mock_search):
     assert mock_search.call_count == 1
     mock_search.assert_called_with(
         collection_name="test",
-        query_vector=[1.0, 2.0, 3.0],
+        query=[1.0, 2.0, 3.0],
+        using=None,
         query_filter=None,
         with_vectors=False,
         limit=3,
@@ -307,7 +308,8 @@ async def test_search_named_vectors(collection, mock_search):
     assert mock_search.call_count == 1
     mock_search.assert_called_with(
         collection_name="test",
-        query_vector=("vector", [1.0, 2.0, 3.0]),
+        query=[1.0, 2.0, 3.0],
+        using="vector",
         query_filter=None,
         with_vectors=False,
         limit=3,
@@ -328,8 +330,9 @@ async def test_search_filter(collection, mock_search):
     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=FieldCondition(key="id", match=MatchValue(value="id1")),
+        query=[1.0, 2.0, 3.0],
+        using="vector",
+        query_filter=Filter(must=[FieldCondition(key="id", match=MatchValue(value="id1"))]),
         with_vectors=False,
         limit=3,
         offset=0,

@@ -0,0 +1,50 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from dataclasses import dataclass
+from typing import Annotated
+
+from pytest import fixture
+
+from semantic_kernel.connectors.qdrant import QdrantCollection
+from semantic_kernel.data.vector import VectorStoreField, vectorstoremodel
+
+
+@vectorstoremodel
+@dataclass
+class Record:
+    id: Annotated[int, VectorStoreField("key")]
+    text: Annotated[str, VectorStoreField("data", is_full_text_indexed=True)]
+    temperature: Annotated[int, VectorStoreField("data", is_indexed=True)]
+    vector: Annotated[list[float] | None, VectorStoreField("vector", dimensions=2)] = None
+
+
+@fixture
+async def local_collection():
+    async with QdrantCollection(record_type=Record, collection_name="test", location=":memory:") as collection:
+        await collection.ensure_collection_exists()
+        await collection.upsert([
+            Record(1, "a", 1, [1.0, 0.2]),
+            Record(2, "a", 5, [0.0, 1.0]),
+            Record(3, "a", 9, [1.0, 1.0]),
+        ])
+        yield collection
+
+
+async def test_local_vector_search(local_collection):
+    results = await local_collection.search(vector=[1.0, 1.0])
+    assert [r.record.id async for r in results.results] == [3, 1, 2]
+
+
+async def test_local_vector_search_single_comparison_filter(local_collection):
+    results = await local_collection.search(vector=[1.0, 1.0], filter="lambda x: x.temperature > 1")
+    assert [r.record.id async for r in results.results] == [3, 2]
+
+
+async def test_local_hybrid_search_single_comparison_filter(local_collection):
+    results = await local_collection.hybrid_search(
+        values=["a"],
+        vector=[1.0, 1.0],
+        additional_property_name="text",
+        filter="lambda x: x.temperature > 1",
+    )
+    assert sorted([r.record.id async for r in results.results]) == [2, 3]