cleanup

apis/python/test/conftest.py

@@ -19,11 +19,11 @@ def no_output(capfd):
     libc.fflush(None)
 
     # Fail if there is any output.
-    # out, err = capfd.readouterr()
-    # if out or err:
-    #     pytest.fail(
-    #         f"Test failed because output was captured. out:\n{out}\nerr:\n{err}"
-    #     )
+    out, err = capfd.readouterr()
+    if out or err:
+        pytest.fail(
+            f"Test failed because output was captured. out:\n{out}\nerr:\n{err}"
+        )
 
 
 @pytest.fixture(scope="session", autouse=True)

src/include/index/ivf_pq_index.h

@@ -861,7 +861,7 @@ class ivf_pq_index {
    * write.
    * @param distance The distance function to use.
    * @param write_to_temp_arrays Whether to write to the temp arrays. True if
-   * inegsting via Python.
+   * ingesting via Python.
    */
   template <
       feature_vector_array Array,
@@ -923,6 +923,16 @@ class ivf_pq_index {
         partition_start);
   }
 
+  /**
+   * @brief Consolidate partitions. This will read the partitioned vectors and
+   * ids from the temp arrays, and write them to the main arrays.
+   *
+   * @param partitions Number of partitions.
+   * @param work_items Number of work items.
+   * @param partition_id_start The starting partition id.
+   * @param partition_id_end The ending partition id.
+   * @param batch The batch size.
+   */
   void consolidate_partitions(
       size_t partitions,
       size_t work_items,

src/include/test/unit_api_ivf_pq_index.cc

@@ -845,6 +845,7 @@ TEST_CASE("write and load index with timestamps", "[api_ivf_pq_index]") {
             scores_finite, ids_finite, {0, 0, 0, 0}, {1, 2, 3, 4});
       }
     }
+
     CHECK(index.temporal_policy().timestamp_start() == 0);
     CHECK(
         index.temporal_policy().timestamp_end() ==
@@ -1003,13 +1004,11 @@ TEST_CASE("write and load index with timestamps", "[api_ivf_pq_index]") {
     auto queries = ColMajorMatrix<feature_type_type>{{{1, 1, 1}}};
     auto&& [scores, ids] =
         index.query(FeatureVectorArray(queries), top_k, nprobe);
-
     check_single_vector_equals(
         scores,
         ids,
         {std::numeric_limits<float>::max()},
         {std::numeric_limits<uint32_t>::max()});
-
     auto&& [scores_with_reranking, ids_with_reranking] =
         index.query(FeatureVectorArray(queries), top_k, nprobe, k_factor);
     check_single_vector_equals(
@@ -1054,6 +1053,7 @@ TEST_CASE("write and load index with timestamps", "[api_ivf_pq_index]") {
         convergence_tolerance);
     CHECK(typed_index.group().get_reassign_ratio() == reassign_ratio);
   }
+
   // Clear history for <= 99 and then load at 99, then make sure we cannot
   // query.
   IndexIVFPQ::clear_history(ctx, index_uri, 99);

src/include/test/unit_ivf_pq_index.cc

@@ -328,12 +328,10 @@ TEMPLATE_TEST_CASE(
     size_t k_nn = 6;
     size_t partitions = 1;
 
-    auto ivf_idx2 = ivf_pq_index<TestType, uint32_t, uint32_t>(
-        /*128,*/ partitions, 2, 4, 1.e-4);
+    auto ivf_idx2 = ivf_pq_index<TestType, uint32_t, uint32_t>(partitions, 2, 4, 1.e-4);
     ivf_idx2.train_ivf(hypercube2);
     ivf_idx2.add(hypercube2, ids);
-    auto ivf_idx4 = ivf_pq_index<TestType, uint32_t, uint32_t>(
-        /*128,*/ partitions, 2, 4, 1.e-4);
+    auto ivf_idx4 = ivf_pq_index<TestType, uint32_t, uint32_t>(partitions, 2, 4, 1.e-4);
     ivf_idx4.train_ivf(hypercube4);
     ivf_idx4.add(hypercube4, ids);