fix: fix sparse vector sort

qdrant_client/local/local_collection.py

@@ -401,7 +401,7 @@ def search(
             scores = calculate_context_scores(query_vector, vectors[: len(self.payload)], distance)
         elif isinstance(query_vector, SparseVector):
             # sparse vector query must be sorted by indices for dot product to work with persisted vectors
-            sort_sparse_vector(query_vector)
+            query_vector = sort_sparse_vector(query_vector)
             sparse_scoring = True
             sparse_vectors = self.sparse_vectors[name]
             scores = calculate_distance_sparse(query_vector, sparse_vectors[: len(self.payload)])

qdrant_client/local/sparse.py

@@ -80,25 +80,3 @@ def sparse_dot_product(vector1: SparseVector, vector2: SparseVector) -> Optional
         return np.float32(result)
     else:
         return None
-
-
-# Generate random sparse vector with given size and density
-# The density is the probability of non-zero value over the whole vector
-def generate_random_sparse_vector(size: int, density: float) -> SparseVector:
-    num_non_zero = int(size * density)
-    indices: List[int] = random.sample(range(size), num_non_zero)
-    values: List[float] = [round(random.random(), 6) for _ in range(num_non_zero)]
-    indices.sort()
-    sparse_vector = SparseVector(indices=indices, values=values)
-    validate_sparse_vector(sparse_vector)
-    return sparse_vector
-
-
-def generate_random_sparse_vector_list(
-    num_vectors: int, vector_size: int, vector_density: float
-) -> List[SparseVector]:
-    sparse_vector_list = []
-    for _ in range(num_vectors):
-        sparse_vector = generate_random_sparse_vector(vector_size, vector_density)
-        sparse_vector_list.append(sparse_vector)
-    return sparse_vector_list

tests/congruence_tests/test_sparse_search.py

@@ -3,16 +3,20 @@
 from qdrant_client.client_base import QdrantBase
 from qdrant_client.conversions.common_types import NamedSparseVector
 from qdrant_client.http.models import models
-from qdrant_client.local.sparse import generate_random_sparse_vector
 from tests.congruence_tests.test_common import (
     COLLECTION_NAME,
     compare_client_results,
     generate_sparse_fixtures,
     init_client,
     init_local,
-    init_remote, sparse_text_vector_size, sparse_image_vector_size, sparse_code_vector_size, sparse_vectors_config,
+    init_remote,
+    sparse_code_vector_size,
+    sparse_image_vector_size,
+    sparse_text_vector_size,
+    sparse_vectors_config,
 )
 from tests.fixtures.filters import one_random_filter_please
+from tests.fixtures.points import generate_random_sparse_vector
 
 
 class TestSimpleSparseSearcher:
@@ -112,7 +116,7 @@ def simple_search_image_select_vector(self, client: QdrantBase) -> List[models.S
         )
 
     def filter_search_text(
-            self, client: QdrantBase, query_filter: models.Filter
+        self, client: QdrantBase, query_filter: models.Filter
     ) -> List[models.ScoredPoint]:
         return client.search(
             collection_name=COLLECTION_NAME,
@@ -123,7 +127,7 @@ def filter_search_text(
         )
 
     def filter_search_text_single(
-            self, client: QdrantBase, query_filter: models.Filter
+        self, client: QdrantBase, query_filter: models.Filter
     ) -> List[models.ScoredPoint]:
         return client.search(
             collection_name=COLLECTION_NAME,

tests/fixtures/points.py

@@ -6,7 +6,8 @@
 
 from qdrant_client._pydantic_compat import construct
 from qdrant_client.http import models
-from qdrant_client.local.sparse import generate_random_sparse_vector
+from qdrant_client.http.models import SparseVector
+from qdrant_client.local.sparse import validate_sparse_vector
 from tests.fixtures.payload import one_random_payload_please
 
 
@@ -24,7 +25,30 @@ def random_vectors(
         raise ValueError("vector_sizes must be int or dict")
 
 
-def random_sparse_vectors(vector_sizes: Union[Dict[str, int], int],) -> models.VectorStruct:
+# Generate random sparse vector with given size and density
+# The density is the probability of non-zero value over the whole vector
+def generate_random_sparse_vector(size: int, density: float) -> SparseVector:
+    num_non_zero = int(size * density)
+    indices: List[int] = random.sample(range(size), num_non_zero)
+    values: List[float] = [round(random.random(), 6) for _ in range(num_non_zero)]
+    sparse_vector = SparseVector(indices=indices, values=values)
+    validate_sparse_vector(sparse_vector)
+    return sparse_vector
+
+
+def generate_random_sparse_vector_list(
+    num_vectors: int, vector_size: int, vector_density: float
+) -> List[SparseVector]:
+    sparse_vector_list = []
+    for _ in range(num_vectors):
+        sparse_vector = generate_random_sparse_vector(vector_size, vector_density)
+        sparse_vector_list.append(sparse_vector)
+    return sparse_vector_list
+
+
+def random_sparse_vectors(
+    vector_sizes: Union[Dict[str, int], int],
+) -> models.VectorStruct:
     vectors = {}
     for vector_name, vector_size in vector_sizes.items():
         # use sparse vectors with 20% density

tests/test_local_persistence.py

@@ -8,7 +8,7 @@
 import qdrant_client
 import qdrant_client.http.models as rest
 from qdrant_client._pydantic_compat import construct
-from qdrant_client.local.sparse import generate_random_sparse_vector_list
+from tests.fixtures.points import generate_random_sparse_vector_list
 
 default_collection_name = "example"