Expose supported brute force metrics in all_neighbors

cpp/src/neighbors/all_neighbors/all_neighbors.cuh

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -32,10 +32,20 @@ GRAPH_BUILD_ALGO check_params_validity(const all_neighbors_params& params,
       auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
                              params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
                              params.metric == cuvs::distance::DistanceType::CosineExpanded ||
-                             params.metric == cuvs::distance::DistanceType::InnerProduct;
-      RAFT_EXPECTS(allowed_metrics,
-                   "Distance metric for all-neighbors build with brute force should be L2Expanded, "
-                   "L2SqrtExpanded, CosineExpanded, or InnerProduct.");
+                             params.metric == cuvs::distance::DistanceType::L1 ||
+                             params.metric == cuvs::distance::DistanceType::L2Unexpanded ||
+                             params.metric == cuvs::distance::DistanceType::L2SqrtUnexpanded ||
+                             params.metric == cuvs::distance::DistanceType::InnerProduct ||
+                             params.metric == cuvs::distance::DistanceType::Linf ||
+                             params.metric == cuvs::distance::DistanceType::Canberra ||
+                             params.metric == cuvs::distance::DistanceType::LpUnexpanded ||
+                             params.metric == cuvs::distance::DistanceType::CorrelationExpanded ||
+                             params.metric == cuvs::distance::DistanceType::JensenShannon;
+      RAFT_EXPECTS(
+        allowed_metrics,
+        "Distance metric for all-neighbors build with brute force should be L2Expanded, "
+        "L2SqrtExpanded, CosineExpanded, L1, L2Unexpanded, L2SqrtUnexpanded, InnerProduct, Linf, "
+        "Canberra, LpUnexpanded, CorrelationExpanded, or JensenShannon.");
     }
     return GRAPH_BUILD_ALGO::BRUTE_FORCE;
   } else if (std::holds_alternative<graph_build_params::nn_descent_params>(

python/cuvs/cuvs/neighbors/all_neighbors/all_neighbors.pyx

@@ -1,5 +1,5 @@
 #
-# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 #
 # cython: language_level=3
@@ -111,11 +111,12 @@ cdef class AllNeighborsParams:
                 )
 
             # Check metric consistency
-            ivf_pq_metric = ivf_pq_params.metric
-            if ivf_pq_metric != metric:
+            metric_type = DISTANCE_TYPES[metric]
+            ivf_pq_metric_type = DISTANCE_TYPES[ivf_pq_params.metric]
+            if ivf_pq_metric_type != metric_type:
                 raise ValueError(
                     f"Metric conflict: AllNeighborsParams metric '{metric}' "
-                    f"does not match IVF-PQ metric '{ivf_pq_metric}'. Please "
+                    f"does not match IVF-PQ metric '{ivf_pq_params.metric}'. Please "
                     f"ensure both use the same metric."
                 )
 
@@ -127,11 +128,12 @@ cdef class AllNeighborsParams:
                 )
 
             # Check metric consistency
-            nn_descent_metric = nn_descent_params.metric
-            if nn_descent_metric != metric:
+            metric_type = DISTANCE_TYPES[metric]
+            nn_descent_metric_type = DISTANCE_TYPES[nn_descent_params.metric]
+            if nn_descent_metric_type != metric_type:
                 raise ValueError(
                     f"Metric conflict: AllNeighborsParams metric '{metric}' "
-                    f"does not match NN-Descent metric '{nn_descent_metric}'. "
+                    f"does not match NN-Descent metric '{nn_descent_params.metric}'. "
                     f"Please ensure both use the same metric."
                 )
 

python/cuvs/cuvs/tests/test_all_neighbors.py

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 #
 
@@ -36,15 +36,40 @@ def make_cosine(
 
 @pytest.mark.parametrize("algo", ["nn_descent", "brute_force", "ivf_pq"])
 @pytest.mark.parametrize("cluster", ["single_cluster", "multi_cluster"])
-@pytest.mark.parametrize("metric", ["sqeuclidean", "cosine"])
+@pytest.mark.parametrize(
+    "metric",
+    [
+        "sqeuclidean",
+        "l2",
+        "cosine",
+        "l1",
+        "inner_product",
+        "chebyshev",
+        "canberra",
+        "minkowski",
+        "correlation",
+        "jensenshannon",
+    ],
+)
 def test_all_neighbors_device_build_quality(algo, cluster, metric):
     """Test device build with quality validation against brute force ground
     truth.
     """
     n_rows, n_cols, k = 7151, 64, 16
 
-    if algo == "ivf_pq" and metric == "cosine":
-        pytest.skip("Skipping IVF-PQ with cosine distance")
+    if algo == "ivf_pq" and metric != "sqeuclidean":
+        pytest.skip(
+            "Skipping IVF-PQ for distance metrics other than sqeuclidean"
+        )
+    elif algo == "nn_descent" and metric not in [
+        "sqeuclidean",
+        "l2",
+        "cosine",
+        "inner_product",
+    ]:
+        pytest.skip(
+            "Skipping NN-Descent for distance metrics other than sqeuclidean, l2, cosine, or inner_product"
+        )
 
     if cluster == "single_cluster":
         overlap_factor = 0
@@ -57,6 +82,21 @@ def test_all_neighbors_device_build_quality(algo, cluster, metric):
         X, _ = make_cosine(
             n_samples=n_rows, n_features=n_cols, random_state=42
         )
+    elif metric == "jensenshannon":
+        # Jensen-Shannon requires non-negative values representing probability distributions
+        X, _ = make_blobs(
+            n_samples=n_rows,
+            n_features=n_cols,
+            centers=10,
+            cluster_std=1.0,
+            center_box=(0.0, 10.0),  # Non-negative values only
+            random_state=42,
+        )
+        # Normalize each row to sum to 1 (probability distribution)
+        X = np.abs(X)  # Ensure non-negative
+        row_sums = X.sum(axis=1, keepdims=True)
+        row_sums[row_sums == 0] = 1  # Avoid division by zero
+        X = X / row_sums
     else:
         X, _ = make_blobs(
             n_samples=n_rows,