[BUG] Test that query result shapes are correct in invariants

chromadb/segment/impl/vector/local_persistent_hnsw.py

@@ -413,6 +413,8 @@ def query_vectors(
         # Overquery by updated and deleted elements layered on the index because they may
         # hide the real nearest neighbors in the hnsw index
         hnsw_k = k + self._curr_batch.update_count + self._curr_batch.delete_count
+        # self._id_to_label contains the ids of the elements in the hnsw index
+        # so its length is the number of elements in the hnsw index
         if hnsw_k > len(self._id_to_label):
             hnsw_k = len(self._id_to_label)
         hnsw_query = VectorQuery(
@@ -472,7 +474,7 @@ def query_vectors(
                     if remaining > 0 and hnsw_pointer < len(curr_hnsw_result):
                         for i in range(
                             hnsw_pointer,
-                            min(len(curr_hnsw_result), hnsw_pointer + remaining + 1),
+                            min(len(curr_hnsw_result), hnsw_pointer + remaining),
                         ):
                             id = curr_hnsw_result[i]["id"]
                             if not self._brute_force_index.has_id(id):

chromadb/test/property/invariants.py

@@ -4,9 +4,7 @@
 from chromadb.db.base import get_sql
 from chromadb.db.impl.sqlite import SqliteDB
 from time import sleep
-
 import psutil
-
 from chromadb.test.property.strategies import NormalizedRecordSet, RecordSet
 from typing import Callable, Optional, Tuple, Union, List, TypeVar, cast
 from typing_extensions import Literal
@@ -261,10 +259,14 @@ def ann_accuracy(
         include=["embeddings", "documents", "metadatas", "distances"],  # type: ignore[list-item]
     )
 
+    _query_results_are_correct_shape(query_results, n_results)
+
+    # Assert fields are not None for type checking
+    assert query_results["ids"] is not None
     assert query_results["distances"] is not None
+    assert query_results["embeddings"] is not None
     assert query_results["documents"] is not None
     assert query_results["metadatas"] is not None
-    assert query_results["embeddings"] is not None
 
     # Dict of ids to indices
     id_to_index = {id: i for i, id in enumerate(normalized_record_set["ids"])}
@@ -324,6 +326,16 @@ def ann_accuracy(
         assert np.allclose(np.sort(distance_result), distance_result)
 
 
+def _query_results_are_correct_shape(
+    query_results: types.QueryResult, n_results: int
+) -> None:
+    for result_type in ["distances", "embeddings", "documents", "metadatas"]:
+        assert query_results[result_type] is not None  # type: ignore[literal-required]
+        assert all(
+            len(result) == n_results for result in query_results[result_type]  # type: ignore[literal-required]
+        )
+
+
 def _total_embedding_queue_log_size(sqlite: SqliteDB) -> int:
     t = Table("embeddings_queue")
     q = sqlite.querybuilder().from_(t)

chromadb/test/property/test_persist.py

@@ -30,6 +30,7 @@
 import tempfile
 from chromadb.api.client import Client as ClientCreator
 from chromadb.utils.embedding_functions import DefaultEmbeddingFunction
+import numpy as np
 
 CreatePersistAPI = Callable[[], ServerAPI]
 
@@ -308,6 +309,55 @@ def test_persist_embeddings_state(
     )  # type: ignore
 
 
+def test_delete_less_than_k(
+    caplog: pytest.LogCaptureFixture, settings: Settings
+) -> None:
+    client = chromadb.Client(settings)
+    state = PersistEmbeddingsStateMachine(settings=settings, client=client)
+    state.initialize(
+        collection=strategies.Collection(
+            name="A00",
+            metadata={
+                "hnsw:construction_ef": 128,
+                "hnsw:search_ef": 128,
+                "hnsw:M": 128,
+                "hnsw:sync_threshold": 3,
+                "hnsw:batch_size": 3,
+            },
+            embedding_function=None,
+            id=UUID("2d3eddc7-2314-45f4-a951-47a9a8e099d2"),
+            dimension=2,
+            dtype=np.float16,
+            known_metadata_keys={},
+            known_document_keywords=[],
+            has_documents=False,
+            has_embeddings=True,
+        )
+    )
+    state.ann_accuracy()
+    state.count()
+    state.fields_match()
+    state.log_size_below_max()
+    state.no_duplicates()
+    (embedding_ids_0,) = state.add_embeddings(record_set={"ids": ["0"], "embeddings": [[0.09765625, 0.430419921875]], "metadatas": [None], "documents": None})  # type: ignore
+    state.ann_accuracy()
+    # recall: 1.0, missing 0 out of 1, accuracy threshold 1e-06
+    state.count()
+    state.fields_match()
+    state.log_size_below_max()
+    state.no_duplicates()
+    embedding_ids_1, embedding_ids_2 = state.add_embeddings(record_set={"ids": ["1", "2"], "embeddings": [[0.20556640625, 0.08978271484375], [-0.1527099609375, 0.291748046875]], "metadatas": [None, None], "documents": None})  # type: ignore
+    state.ann_accuracy()
+    # recall: 1.0, missing 0 out of 3, accuracy threshold 1e-06
+    state.count()
+    state.fields_match()
+    state.log_size_below_max()
+    state.no_duplicates()
+    state.delete_by_ids(ids=[embedding_ids_2])
+    state.ann_accuracy()
+    state.teardown()
+
+
 # Ideally this scenario would be exercised by Hypothesis, but most runs don't seem to trigger this particular state.
 def test_delete_add_after_persist(settings: Settings) -> None:
     client = chromadb.Client(settings)

rust/worker/src/execution/operators/merge_knn_results.rs

@@ -205,7 +205,7 @@ fn merge_results(
     let mut brute_force_index = 0;
 
     // TODO: This doesn't have to clone the user IDs, but it's easier for now
-    while (result_user_ids.len() <= k)
+    while (result_user_ids.len() < k)
         && (hnsw_index < hnsw_result_user_ids.len()
             || brute_force_index < brute_force_result_user_ids.len())
     {