Add missing HNSW bindings

c/include/cuvs/neighbors/hnsw.h

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -109,7 +109,7 @@ CUVS_EXPORT cuvsError_t cuvsHnswAceParamsDestroy(cuvsHnswAceParams_t params);
 struct cuvsHnswIndexParams {
   /* hierarchy of the hnsw index */
   enum cuvsHnswHierarchy hierarchy;
-  /** Size of the candidate list during hierarchy construction when hierarchy is `CPU`*/
+  /** Maximum candidate list size used during index construction. */
   int ef_construction;
   /** Number of host threads to use to construct hierarchy when hierarchy is `CPU` or `GPU`.
       When the value is 0, the number of threads is automatically determined to the
@@ -119,15 +119,15 @@ struct cuvsHnswIndexParams {
       is parallelized with the help of CPU threads.
   */
   int num_threads;
-  /** HNSW M parameter: number of bi-directional links per node (used when building with ACE).
-   *  graph_degree = m * 2, intermediate_graph_degree = m * 3.
+  /** HNSW M parameter: number of bi-directional links per node. When the graph is built on the GPU,
+   *  this parameter is used to derive the internal CAGRA graph build parameters.
    */
   size_t M;
   /** Distance type for the index. */
   cuvsDistanceType metric;
   /**
-   * Optional: specify ACE parameters for building HNSW index using ACE algorithm.
-   * Set to nullptr for default behavior (from_cagra conversion).
+   * Optional ACE parameters for out-of-core graph construction.
+   * Set to nullptr to select the graph build algorithm automatically.
    */
   cuvsHnswAceParams_t ace_params;
 };
@@ -285,22 +285,20 @@ CUVS_EXPORT cuvsError_t cuvsHnswFromCagraWithDataset(cuvsResources_t res,
  */
 
 /**
- * @defgroup hnsw_c_index_build Build HNSW index using ACE algorithm
+ * @defgroup hnsw_c_index_build Build an HNSW index
  * @{
  */
 
 /**
- * @brief Build an HNSW index using ACE (Augmented Core Extraction) algorithm.
+ * @brief Build an HNSW index from HNSW parameters.
  *
- * ACE enables building HNSW indexes for datasets too large to fit in GPU memory by:
- * 1. Partitioning the dataset using balanced k-means into core and augmented partitions
- * 2. Building sub-indexes for each partition independently
- * 3. Concatenating sub-graphs into a final unified index
+ * The graph is built on the GPU and converted to an HNSW index that can be searched on the CPU.
+ * The graph build algorithm is selected automatically unless explicit ACE parameters are provided.
  *
  * NOTE: This function requires CUDA to be available at runtime.
  *
  * @param[in] res cuvsResources_t opaque C handle
- * @param[in] params cuvsHnswIndexParams_t with ACE parameters configured
+ * @param[in] params cuvsHnswIndexParams_t with HNSW build parameters
  * @param[in] dataset DLManagedTensor* host dataset to build index from
  * @param[out] index cuvsHnswIndex_t to return the built HNSW index
  *
@@ -314,18 +312,10 @@ CUVS_EXPORT cuvsError_t cuvsHnswFromCagraWithDataset(cuvsResources_t res,
  * cuvsResources_t res;
  * cuvsResourcesCreate(&res);
  *
- * // Create ACE parameters
- * cuvsHnswAceParams_t ace_params;
- * cuvsHnswAceParamsCreate(&ace_params);
- * ace_params->npartitions = 4;
- * ace_params->use_disk = true;
- * ace_params->build_dir = "/tmp/hnsw_ace_build";
- *
  * // Create index parameters
  * cuvsHnswIndexParams_t params;
  * cuvsHnswIndexParamsCreate(&params);
  * params->hierarchy = GPU;
- * params->ace_params = ace_params;
  * params->M = 32;
  * params->ef_construction = 120;
  *
@@ -340,7 +330,6 @@ CUVS_EXPORT cuvsError_t cuvsHnswFromCagraWithDataset(cuvsResources_t res,
  * cuvsHnswBuild(res, params, &dataset, hnsw_index);
  *
  * // Clean up
- * cuvsHnswAceParamsDestroy(ace_params);
  * cuvsHnswIndexParamsDestroy(params);
  * cuvsHnswIndexDestroy(hnsw_index);
  * cuvsResourcesDestroy(res);

c/src/neighbors/hnsw.cpp

@@ -1,6 +1,6 @@
 
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -37,15 +37,17 @@ void _build(cuvsResources_t res,
   cpp_params.M               = params->M;
   cpp_params.metric          = static_cast<cuvs::distance::DistanceType>(params->metric);
 
-  // Configure ACE parameters
-  RAFT_EXPECTS(params->ace_params != nullptr, "ACE parameters must be set for hnsw::build");
-  auto ace_params                 = cuvs::neighbors::hnsw::graph_build_params::ace_params();
-  ace_params.npartitions          = params->ace_params->npartitions;
-  ace_params.build_dir            = params->ace_params->build_dir ? params->ace_params->build_dir : "/tmp/hnsw_ace_build";
-  ace_params.use_disk             = params->ace_params->use_disk;
-  ace_params.max_host_memory_gb   = params->ace_params->max_host_memory_gb;
-  ace_params.max_gpu_memory_gb    = params->ace_params->max_gpu_memory_gb;
-  cpp_params.graph_build_params   = ace_params;
+  if (params->ace_params != nullptr) {
+    auto ace_params               = cuvs::neighbors::hnsw::graph_build_params::ace_params();
+    ace_params.npartitions        = params->ace_params->npartitions;
+    ace_params.build_dir          = params->ace_params->build_dir
+                                      ? params->ace_params->build_dir
+                                      : "/tmp/hnsw_ace_build";
+    ace_params.use_disk           = params->ace_params->use_disk;
+    ace_params.max_host_memory_gb = params->ace_params->max_host_memory_gb;
+    ace_params.max_gpu_memory_gb  = params->ace_params->max_gpu_memory_gb;
+    cpp_params.graph_build_params = ace_params;
+  }
 
   using dataset_mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
   auto dataset_mds          = cuvs::core::from_dlpack<dataset_mdspan_type>(dataset_tensor);

c/tests/neighbors/ann_hnsw_c.cu

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -123,6 +123,37 @@ TEST(CagraHnswC, BuildSearch)
   cuvsResourcesDestroy(res);
 }
 
+TEST(HnswC, BuildWithoutAce)
+{
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = dataset;
+  dataset_tensor.dl_tensor.device.device_type = kDLCPU;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {4, 2};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = nullptr;
+
+  cuvsHnswIndexParams_t hnsw_params;
+  cuvsHnswIndexParamsCreate(&hnsw_params);
+  hnsw_params->M               = 2;
+  hnsw_params->ef_construction = 100;
+
+  cuvsHnswIndex_t hnsw_index;
+  cuvsHnswIndexCreate(&hnsw_index);
+
+  ASSERT_EQ(cuvsHnswBuild(res, hnsw_params, &dataset_tensor, hnsw_index), CUVS_SUCCESS);
+
+  cuvsHnswIndexParamsDestroy(hnsw_params);
+  cuvsHnswIndexDestroy(hnsw_index);
+  cuvsResourcesDestroy(res);
+}
+
 TEST(HnswAceC, BuildSearch)
 {
   // create cuvsResources_t

fern/pages/c_api/c-api-neighbors-hnsw.md

@@ -282,12 +282,12 @@ cuvsHnswIndex_t hnsw_index);
 
 [`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
 
-## Build HNSW index using ACE algorithm
+## Build an HNSW index
 
 <a id="cuvshnswbuild"></a>
 ### cuvsHnswBuild
 
-Build an HNSW index using ACE (Augmented Core Extraction) algorithm.
+Build an HNSW index from HNSW parameters.
 
 ```c
 cuvsError_t cuvsHnswBuild(cuvsResources_t res,
@@ -296,11 +296,8 @@ DLManagedTensor* dataset,
 cuvsHnswIndex_t index);
 ```
 
-ACE enables building HNSW indexes for datasets too large to fit in GPU memory by:
-
-1. Partitioning the dataset using balanced k-means into core and augmented partitions
-2. Building sub-indexes for each partition independently
-3. Concatenating sub-graphs into a final unified index
+The graph is built on the GPU and converted to an HNSW index that can be searched on the CPU.
+The graph build algorithm is selected automatically unless explicit ACE parameters are provided.
 
 NOTE: This function requires CUDA to be available at runtime.
 
@@ -309,7 +306,7 @@ NOTE: This function requires CUDA to be available at runtime.
 | Name | Direction | Type | Description |
 | --- | --- | --- | --- |
 | `res` | in | [`cuvsResources_t`](/api-reference/c-api-core-c-api#cuvsresources-t) | cuvsResources_t opaque C handle |
-| `params` | in | `cuvsHnswIndexParams_t` | cuvsHnswIndexParams_t with ACE parameters configured |
+| `params` | in | `cuvsHnswIndexParams_t` | cuvsHnswIndexParams_t with HNSW build parameters |
 | `dataset` | in | `DLManagedTensor*` | DLManagedTensor* host dataset to build index from |
 | `index` | out | [`cuvsHnswIndex_t`](/api-reference/c-api-neighbors-hnsw#cuvshnswindex) | cuvsHnswIndex_t to return the built HNSW index |
 

fern/pages/java_api/java-api-com-nvidia-cuvs-hnswindex.md

@@ -101,21 +101,16 @@ _Source: `java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java:53`_
 static HnswIndex build(CuVSResources resources, HnswIndexParams hnswParams, CuVSMatrix dataset) throws Throwable
 ```
 
-Builds an HNSW index using the ACE (Augmented Core Extraction) algorithm.
-
-ACE enables building HNSW indexes for datasets too large to fit in GPU
-memory by partitioning the dataset and building sub-indexes for each
-partition independently.
-
-NOTE: This method requires `hnswParams.getAceParams()` to be set with
-an instance of HnswAceParams.
+Builds an HNSW index from HNSW parameters. The graph is built on the GPU and converted to an
+HNSW index that can be searched on the CPU. The graph build algorithm is selected automatically
+unless explicit ACE parameters are provided.
 
 **Parameters**
 
 | Name | Description |
 | --- | --- |
 | `resources` | The CuVS resources |
-| `hnswParams` | Parameters for the HNSW index with ACE configuration |
+| `hnswParams` | Parameters for the HNSW index |
 | `dataset` | The dataset to build the index from |
 
 **Returns**

fern/pages/java_api/java-api-com-nvidia-cuvs-hnswindexparams.md

@@ -83,8 +83,7 @@ public long getM()
 ```
 
 Gets the HNSW M parameter: number of bi-directional links per node
-(used when building with ACE). graph_degree = m * 2,
-intermediate_graph_degree = m * 3.
+used to derive the internal graph build parameters for GPU construction.
 
 **Returns**
 
@@ -112,7 +111,8 @@ _Source: `java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java:142`
 public HnswAceParams getAceParams()
 ```
 
-Gets the ACE parameters for building HNSW index using ACE algorithm.
+Gets the optional ACE parameters for explicit out-of-core graph construction. When not set, the
+graph build algorithm is selected automatically.
 
 **Returns**
 
@@ -159,14 +159,13 @@ _Source: `java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java:202`
 public Builder withEfConstruction(int efConstruction)
 ```
 
-Sets the size of the candidate list during hierarchy construction when
-hierarchy is `CPU`.
+Sets the maximum candidate list size used during index construction.
 
 **Parameters**
 
 | Name | Description |
 | --- | --- |
-| `efConstruction` | the size of the candidate list during hierarchy construction when hierarchy is `CPU` |
+| `efConstruction` | the maximum candidate list size used during construction |
 
 **Returns**
 
@@ -222,8 +221,7 @@ public Builder withM(long m)
 ```
 
 Sets the HNSW M parameter: number of bi-directional links per node
-(used when building with ACE). graph_degree = m * 2,
-intermediate_graph_degree = m * 3.
+used to derive the internal graph build parameters for GPU construction.
 
 **Parameters**
 
@@ -263,7 +261,8 @@ _Source: `java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java:262`
 public Builder withAceParams(HnswAceParams aceParams)
 ```
 
-Sets the ACE parameters for building HNSW index using ACE algorithm.
+Sets optional ACE parameters for explicit out-of-core graph construction. When not set, the graph
+build algorithm is selected automatically.
 
 **Parameters**
 

fern/pages/java_api/java-api-com-nvidia-cuvs-spi-cuvsprovider.md

@@ -229,14 +229,14 @@ _Source: `java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java:123
 HnswIndex hnswIndexBuild(CuVSResources resources, HnswIndexParams hnswParams, CuVSMatrix dataset) throws Throwable
 ```
 
-Builds an HNSW index using the ACE (Augmented Core Extraction) algorithm.
+Builds an HNSW index from HNSW parameters using GPU graph construction.
 
 **Parameters**
 
 | Name | Description |
 | --- | --- |
 | `resources` | The CuVS resources |
-| `hnswParams` | Parameters for the HNSW index with ACE configuration |
+| `hnswParams` | Parameters for the HNSW index |
 | `dataset` | The dataset to build the index from |
 
 **Returns**

fern/pages/python_api/python-api-neighbors-hnsw.md

@@ -87,11 +87,11 @@ Parameters to build index for HNSW nearest neighbor search
 | Name | Type | Description |
 | --- | --- | --- |
 | `hierarchy` | `string, default = "gpu" (optional)` | The hierarchy of the HNSW index.<br />Valid values are ["none", "cpu", "gpu"].<br />- "none": No hierarchy is built.<br />- "cpu": Hierarchy is built using CPU.<br />- "gpu": Hierarchy is built using GPU. |
-| `ef_construction` | `int, default = 200 (optional)` | Maximum number of candidate list size used during construction when hierarchy is `cpu`. |
+| `ef_construction` | `int, default = 200 (optional)` | Maximum candidate list size used during index construction. |
 | `num_threads` | `int, default = 0 (optional)` | Number of CPU threads used to increase construction parallelism when hierarchy is `cpu` or `gpu`. When the value is 0, the number of threads is automatically determined to the maximum number of threads available.<br />NOTE: When hierarchy is `gpu`, while the majority of the work is done on the GPU, initialization of the HNSW index itself and some other work is parallelized with the help of CPU threads. |
-| `M` | `int, default = 32 (optional)` | HNSW M parameter: number of bi-directional links per node (used when building with ACE). graph_degree = m * 2, intermediate_graph_degree = m * 3. |
+| `M` | `int, default = 32 (optional)` | HNSW M parameter: number of bi-directional links per node. When the graph is built on the GPU, this parameter is used to derive the internal CAGRA graph build parameters. |
 | `metric` | `string, default = "sqeuclidean" (optional)` | Distance metric to use.<br />Valid values: ["sqeuclidean", "inner_product"] |
-| `ace_params` | `AceParams, default = None (optional)` | ACE parameters for building HNSW index using ACE algorithm. If set, enables the build() function to use ACE for index construction. |
+| `ace_params` | `AceParams, default = None (optional)` | Explicit ACE parameters for out-of-core graph construction. When not set, the graph build algorithm is selected automatically. |
 
 **Constructor**
 
@@ -200,20 +200,17 @@ def num_threads(self)
 def build(IndexParams index_params, dataset, resources=None)
 ```
 
-Build an HNSW index using the ACE (Augmented Core Extraction) algorithm.
+Build an HNSW index from HNSW parameters.
 
-ACE enables building HNSW indices for datasets too large to fit in GPU
-memory by partitioning the dataset and building sub-indices for each
-partition independently.
-
-NOTE: This function requires `index_params.ace_params` to be set with
-an instance of AceParams.
+The graph is built on the GPU and converted to an HNSW index that can be
+searched on the CPU. The graph build algorithm is selected automatically
+unless explicit ACE parameters are provided.
 
 **Parameters**
 
 | Name | Type | Description |
 | --- | --- | --- |
-| `index_params` | `IndexParams` | Parameters for the HNSW index with ACE configuration. Must have `ace_params` set. |
+| `index_params` | `IndexParams` | Parameters for the HNSW index. |
 | `dataset` | `Host array interface compliant matrix shape (n_samples, dim)` | Supported dtype [float32, float16, int8, uint8] |
 | `resources` | `cuvs.common.Resources, optional` |  |
 
@@ -234,17 +231,9 @@ an instance of AceParams.
 >>> dataset = np.random.random_sample((n_samples, n_features),
 ...                                   dtype=np.float32)
 >>>
->>> # Create ACE parameters
->>> ace_params = hnsw.AceParams(
-...     npartitions=4,
-...     use_disk=True,
-...     build_dir="/tmp/hnsw_ace_build"
-... )
->>>
->>> # Create index parameters with ACE
+>>> # Create HNSW index parameters
 >>> index_params = hnsw.IndexParams(
 ...     hierarchy="gpu",
-...     ace_params=ace_params,
 ...     ef_construction=120,
 ...     M=32,
 ...     metric="sqeuclidean"