fix: do not modify input structs in-place (#715)

* fix: do not modify input structs in-place

* fix: regen async

qdrant_client/local/async_qdrant_local.py

@@ -14,6 +14,7 @@
 import logging
 import os
 import shutil
+from copy import deepcopy
 from io import TextIOWrapper
 from typing import (
     Any,
@@ -290,6 +291,7 @@ def input_into_vector(vector_input: types.VectorInput) -> types.VectorInput:
             else:
                 return vector_input
 
+        query = deepcopy(query)
         if isinstance(query, rest_models.NearestQuery):
             query.nearest = input_into_vector(query.nearest)
         elif isinstance(query, rest_models.RecommendQuery):
@@ -353,6 +355,7 @@ def _resolve_prefetch_input(
     ) -> types.Prefetch:
         if prefetch.query is None:
             return prefetch
+        prefetch = deepcopy(prefetch)
         (query, mentioned_ids) = self._resolve_query_input(
             collection_name, prefetch.query, prefetch.using, prefetch.lookup_from
         )

qdrant_client/local/local_collection.py

@@ -661,8 +661,6 @@ def query_points(
 
         Assumes all vectors have been homogenized so that there are no ids in the inputs
         """
-        scored_points = []
-
         prefetches = []
         if prefetch is not None:
             prefetches = prefetch if isinstance(prefetch, list) else [prefetch]
@@ -709,11 +707,6 @@ def _prefetch(self, prefetch: types.Prefetch, offset: int) -> List[types.ScoredP
             )
 
         if len(inner_prefetches) > 0:
-            # Recursive case: inner prefetches
-            prefetches = (
-                prefetch.prefetch if isinstance(prefetch.prefetch, list) else [prefetch.prefetch]
-            )
-
             sources = [
                 self._prefetch(inner_prefetch, offset) for inner_prefetch in inner_prefetches
             ]
@@ -782,7 +775,7 @@ def _merge_sources(
                     sources_ids.add(point.id)
 
             if len(sources_ids) == 0:
-                # no need to perform a query if there no matches for the sources
+                # no need to perform a query if there are no matches for the sources
                 return []
             else:
                 filter_with_sources = _include_ids_in_filter(query_filter, list(sources_ids))
@@ -2173,6 +2166,7 @@ def ignore_mentioned_ids_filter(
     if query_filter is None:
         query_filter = models.Filter(must_not=[ignore_mentioned_ids])
     else:
+        query_filter = deepcopy(query_filter)
         if query_filter.must_not is None:
             query_filter.must_not = [ignore_mentioned_ids]
         else:
@@ -2192,6 +2186,7 @@ def _include_ids_in_filter(
     if query_filter is None:
         query_filter = models.Filter(must=[include_ids])
     else:
+        query_filter = deepcopy(query_filter)
         if query_filter.must is None:
             query_filter.must = [include_ids]
         else:

qdrant_client/local/qdrant_local.py

@@ -3,6 +3,7 @@
 import logging
 import os
 import shutil
+from copy import deepcopy
 from io import TextIOWrapper
 from typing import (
     Any,
@@ -305,6 +306,7 @@ def input_into_vector(
             else:
                 return vector_input
 
+        query = deepcopy(query)
         if isinstance(query, rest_models.NearestQuery):
             query.nearest = input_into_vector(query.nearest)
 
@@ -375,6 +377,7 @@ def _resolve_prefetch_input(
         if prefetch.query is None:
             return prefetch
 
+        prefetch = deepcopy(prefetch)
         query, mentioned_ids = self._resolve_query_input(
             collection_name,
             prefetch.query,

tests/congruence_tests/test_query.py

@@ -25,7 +25,11 @@
     multi_vector_config,
 )
 from tests.fixtures.filters import one_random_filter_please
-from tests.fixtures.points import generate_random_sparse_vector, generate_random_multivector
+from tests.fixtures.points import (
+    generate_random_sparse_vector,
+    generate_random_multivector,
+    generate_points,
+)
 from tests.utils import read_version
 
 SECONDARY_COLLECTION_NAME = "congruence_secondary_collection"
@@ -1165,3 +1169,69 @@ def test_flat_query_multivector_interface(prefer_grpc):
     init_client(remote_client, fixture_points, vectors_config=multi_vector_config)
 
     compare_client_results(local_client, remote_client, searcher.multivec_query_text)
+
+
+@pytest.mark.parametrize("prefer_grpc", (False, True))
+def test_original_input_persistence(prefer_grpc):
+    num_points = 50
+    vectors_config = {"text": models.VectorParams(size=50, distance=models.Distance.COSINE)}
+    sparse_vectors_config = {"sparse-text": models.SparseVectorParams()}
+    fixture_points = generate_fixtures(vectors_sizes={"text": 50}, num=num_points)
+    sparse_fixture_points = generate_sparse_fixtures(num=num_points)
+    points = [
+        models.PointStruct(
+            id=point.id,
+            payload=point.payload,
+            vector={
+                "text": point.vector["text"],
+                "sparse-text": sparse_point.vector["sparse-text"],
+            },
+        )
+        for point, sparse_point in zip(fixture_points, sparse_fixture_points)
+    ]
+    dense_vector_name = "text"
+    sparse_vector_name = "sparse-text"
+
+    local_client = init_local()
+    init_client(
+        local_client,
+        points,
+        vectors_config=vectors_config,
+        sparse_vectors_config=sparse_vectors_config,
+    )
+    remote_client = init_remote(prefer_grpc=prefer_grpc)
+    init_client(
+        remote_client,
+        points,
+        vectors_config=vectors_config,
+        sparse_vectors_config=sparse_vectors_config,
+    )
+
+    point_id = 1
+    shared_instance = models.RecommendInput(positive=[point_id], negative=[])
+    prefetch = [
+        models.Prefetch(
+            query=models.RecommendQuery(recommend=shared_instance),
+            using=sparse_vector_name,
+        ),
+    ]
+    local_client.query_points(
+        collection_name=COLLECTION_NAME,
+        prefetch=prefetch,
+        query=models.RecommendQuery(recommend=shared_instance),
+        using=dense_vector_name,
+    )
+
+    shared_instance = models.RecommendInput(positive=[point_id], negative=[])
+    prefetch = [
+        models.Prefetch(
+            query=models.RecommendQuery(recommend=shared_instance),
+            using=sparse_vector_name,
+        ),
+    ]
+    remote_client.query_points(
+        collection_name=COLLECTION_NAME,
+        prefetch=prefetch,
+        query=models.RecommendQuery(recommend=shared_instance),
+        using=dense_vector_name,
+    )