Adding new docs link(s) to readme. Adding landing page for API refs

Dockerfile

@@ -34,7 +34,7 @@ RUN echo "  Building cuVS Docker image with:" && \
 # Container metadata
 LABEL maintainer="RAPIDS cuVS Team"
 LABEL description="RAPIDS cuVS - Vector Search and Clustering on GPU"
-LABEL org.opencontainers.image.source="https://github.com/rapidsai/cuvs"
+LABEL org.opencontainers.image.source="https://github.com/nvidia/cuvs"
 LABEL org.opencontainers.image.usage="docker run --gpus all -it <image>"
 
 # Environment setup

README.md

@@ -12,14 +12,14 @@
 
 ## Useful Resources
 
-- [Documentation](https://docs.rapids.ai/api/cuvs/nightly/): Library documentation.
-- [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build): Instructions for installing and building cuVS.
-- [Getting Started Guide](https://docs.rapids.ai/api/cuvs/nightly/getting_started): Guide to getting started with cuVS.
-- [Code Examples](https://github.com/rapidsai/cuvs/tree/HEAD/examples): Self-contained Code Examples.
-- [API Reference Documentation](https://docs.rapids.ai/api/cuvs/nightly/api_docs): API Documentation.
+- [Documentation](https://docs.nvidia.com/cuvs): Library documentation.
+- [Build and Install Guide](https://docs.nvidia.com/cuvs/installation): Instructions for installing and building cuVS.
+- [Getting Started Guide](https://docs.nvidia.com/cuvs/getting-started): Guide to getting started with cuVS.
+- [Code Examples](https://github.com/nvidia/cuvs/tree/HEAD/examples): Self-contained Code Examples.
+- [API Reference Documentation](https://docs.nvidia.com/cuvs/api_reference): API Documentation.
 - [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate.
-- [GitHub repository](https://github.com/rapidsai/cuvs): Download the cuVS source code.
-- [Issue tracker](https://github.com/rapidsai/cuvs/issues): Report issues or request features.
+- [GitHub repository](https://github.com/nvidia/cuvs): Download the cuVS source code.
+- [Issue tracker](https://github.com/nvidia/cuvs/issues): Report issues or request features.
 
 ## What is cuVS?
 
@@ -114,7 +114,7 @@ cagra::index_params index_params;
 auto index = cagra::build(res, index_params, dataset);
 ```
 
-For more code examples of the C++ APIs, including drop-in Cmake project templates, please refer to the [C++ examples](https://github.com/rapidsai/cuvs/tree/HEAD/examples) directory in the codebase.
+For more code examples of the C++ APIs, including drop-in Cmake project templates, please refer to the [C++ examples](https://github.com/nvidia/cuvs/tree/HEAD/examples) directory in the codebase.
 
 ### C API
 
@@ -139,7 +139,7 @@ cuvsCagraIndexParamsDestroy(index_params);
 cuvsResourcesDestroy(res);
 ```
 
-For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/main/examples/c)
+For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/nvidia/cuvs/tree/main/examples/c)
 
 ### Rust API
 
@@ -202,7 +202,7 @@ fn cagra_example() -> Result<()> {
 }
 ```
 
-For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/main/examples/rust).
+For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/nvidia/cuvs/tree/main/examples/rust).
 
 ## Contributing
 

ci/build_java.sh

@@ -12,7 +12,7 @@ export SCCACHE_S3_USE_PREPROCESSOR_CACHE_MODE=true
 
 # TODO: Remove this argument-handling when build and test workflows are separated,
 #       and test_java.sh no longer calls build_java.sh
-#       ref: https://github.com/rapidsai/cuvs/issues/868
+#       ref: https://github.com/nvidia/cuvs/issues/868
 EXTRA_BUILD_ARGS=()
 if [[ "${1:-}" == "--run-java-tests" ]]; then
   EXTRA_BUILD_ARGS+=("--run-java-tests")

ci/check_c_abi/pyproject.toml

@@ -16,7 +16,7 @@ dependencies = ["libclang", "msgspec", "termcolor"]
 check-c-abi = "check_c_abi.main:main_cli"
 
 [project.urls]
-Homepage = "https://github.com/rapidsai/cuvs"
+Homepage = "https://github.com/nvidia/cuvs"
 
 [tool.setuptools.dynamic]
 version = { file = "VERSION" }

ci/test_java.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 
 set -euo pipefail
@@ -17,7 +17,7 @@ RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 export RAPIDS_CUDA_MAJOR
 
 # TODO: switch to installing pre-built artifacts instead of rebuilding in test jobs
-#       ref: https://github.com/rapidsai/cuvs/issues/868
+#       ref: https://github.com/nvidia/cuvs/issues/868
 ci/build_java.sh --run-java-tests
 
 rapids-logger "Test script exiting with value: $EXITCODE"

cpp/bench/ann/src/diskann/diskann_wrapper.h

@@ -35,7 +35,7 @@ diskann::Metric parse_metric_to_diskann(cuvs::bench::Metric metric)
   }
 }
 
-// TODO (tarangj): Remaining features are tracked at https://github.com/rapidsai/cuvs/issues/656
+// TODO (tarangj): Remaining features are tracked at https://github.com/nvidia/cuvs/issues/656
 template <typename T>
 class diskann_memory : public algo<T> {
  public:

cpp/include/cuvs/cluster/kmeans.hpp

@@ -181,7 +181,7 @@ enum class kmeans_type { KMeans = 0, KMeansBalanced = 1 };
  * @brief Find clusters with k-means algorithm using batched processing of host data.
  *
  * TODO: Evaluate replacing the extent type with int64_t. Reference issue:
- * https://github.com/rapidsai/cuvs/issues/1961
+ * https://github.com/nvidia/cuvs/issues/1961
  *
  * This overload supports out-of-core computation where the dataset resides
  * on the host. Data is processed in GPU-sized batches, streaming from host to device.

cpp/include/cuvs/cluster/spectral.hpp

@@ -49,7 +49,7 @@ struct params {
  * @{
  */
 
-// TODO: int64_t nnz support (see https://github.com/rapidsai/cuvs/issues/1484)
+// TODO: int64_t nnz support (see https://github.com/nvidia/cuvs/issues/1484)
 
 /**
  * @brief Perform spectral clustering on a connectivity graph

cpp/include/cuvs/neighbors/common.hpp

@@ -758,7 +758,7 @@ constexpr static IdxT kInvalidRecord =
  * @tparam IdxT The index type for source indices
  * @tparam SizeT The size type
  *
- * TODO: Make this struct internal (tracking issue: https://github.com/rapidsai/cuvs/issues/1726)
+ * TODO: Make this struct internal (tracking issue: https://github.com/nvidia/cuvs/issues/1726)
  */
 template <typename ValueT, typename IdxT, typename SizeT = uint32_t>
 struct list_base {

cpp/include/cuvs/neighbors/hnsw.hpp

@@ -750,7 +750,7 @@ struct search_params : cuvs::neighbors::search_params {
  * @}
  */
 
-// TODO: Filtered Search APIs: https://github.com/rapidsai/cuvs/issues/363
+// TODO: Filtered Search APIs: https://github.com/nvidia/cuvs/issues/363
 
 /**
  * @defgroup hnsw_cpp_index_search Search hnswlib index

cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh

@@ -116,7 +116,7 @@ RAFT_KERNEL merge_subgraphs_kernel(IdxT* cluster_data_indices,
         // to each other after sorting by distances. Thus, for now we sweep a neighboring window of
         // size 4 or sweep the entire row to check for duplicates, and keep the first occurrence
         // only.
-        // related issue: https://github.com/rapidsai/cuvs/issues/1056
+        // related issue: https://github.com/nvidia/cuvs/issues/1056
         // uniqueMask[colId] = static_cast<int16_t>(blockValues[colId] != blockValues[colId - 1]);
 
         int is_unique = 1;

cpp/src/neighbors/detail/cagra/cagra_build.cuh

@@ -94,7 +94,7 @@ void ace_get_partition_labels(
 
   // Sampling vectors from dataset. Uses float conversion on host instead of
   // raft::matrix::sample_rows to minimize GPU memory usage.
-  // TODO(julianmi): Switch to sample_rows when https://github.com/rapidsai/cuvs/issues/1461 is
+  // TODO(julianmi): Switch to sample_rows when https://github.com/nvidia/cuvs/issues/1461 is
   // addressed.
   size_t n_samples         = dataset_size * sampling_rate;
   const size_t min_samples = 100 * n_partitions;

cpp/src/neighbors/detail/nn_descent.cuh

@@ -539,7 +539,7 @@ __device__ __forceinline__ void calculate_metric(float* s_distances,
         s_distances[i] = 0.0;
         int n1         = row_neighbors[row_id];
         int n2         = col_neighbors[col_id];
-        // TODO: https://github.com/rapidsai/cuvs/issues/1127
+        // TODO: https://github.com/nvidia/cuvs/issues/1127
         const uint8_t* data_n1 = reinterpret_cast<const uint8_t*>(data) + n1 * data_dim;
         const uint8_t* data_n2 = reinterpret_cast<const uint8_t*>(data) + n2 * data_dim;
         for (int d = 0; d < data_dim; d++) {
@@ -550,7 +550,7 @@ __device__ __forceinline__ void calculate_metric(float* s_distances,
         s_distances[i] =
           l2_norms[row_neighbors[row_id]] + l2_norms[col_neighbors[col_id]] - 2.0 * s_distances[i];
         // for fp32 vs fp16 precision differences resulting in negative distances when distance
-        // should be 0 related issue: https://github.com/rapidsai/cuvs/issues/991
+        // should be 0 related issue: https://github.com/nvidia/cuvs/issues/991
         s_distances[i] = s_distances[i] < 0.0f ? 0.0f : s_distances[i];
         if (!can_postprocess_dist && metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
           s_distances[i] = sqrtf(s_distances[i]);

cpp/src/neighbors/detail/vamana/vamana_build.cuh

@@ -216,7 +216,7 @@ void batched_insert_vamana(
 
   // Random medoid has minor impact on recall
   // TODO: use heuristic for better medoid selection, issue:
-  // https://github.com/rapidsai/cuvs/issues/355
+  // https://github.com/nvidia/cuvs/issues/355
   *medoid_id = rand() % N;
 
   // size of current batch of inserts, increases logarithmically until max_batchsize

cpp/src/neighbors/detail/vpq_dataset.cuh

@@ -401,7 +401,7 @@ __launch_bounds__(BlockSize) RAFT_KERNEL process_and_fill_codes_kernel(
 /**
  * Note: `inline_vq_labels` should only be used for CAGRA-Q compatibility or internal use-cases.
  * Otherwise, vq_labels should be preferred.
- * Issue: https://github.com/rapidsai/cuvs/issues/1722
+ * Issue: https://github.com/nvidia/cuvs/issues/1722
  */
 template <typename MathT, typename IdxT, typename DatasetT>
 void process_and_fill_codes(

cpp/src/neighbors/mg/snmg.cuh

@@ -607,7 +607,7 @@ void search(const raft::resources& clique,
       cuvs::core::omp::check_threads(index.num_ranks_);
 // Each rank gets its own thread; that thread handles all batches for that rank sequentially.
 // This prevents concurrent access to the same GPU from multiple threads. (see
-// https://github.com/rapidsai/cuvs/issues/1720)
+// https://github.com/nvidia/cuvs/issues/1720)
 #pragma omp parallel for num_threads(index.num_ranks_)
       for (int rank = 0; rank < index.num_ranks_; rank++) {
         for (int64_t batch_idx = rank; batch_idx < n_batches; batch_idx += index.num_ranks_) {

cpp/tests/neighbors/ann_cagra/bug_issue_93_reproducer.cu

@@ -2,7 +2,7 @@
  * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION.
  * SPDX-License-Identifier: Apache-2.0
  *
- * Reproducer for https://github.com/rapidsai/cuvs-lucene/issues/93
+ * Reproducer for https://github.com/nvidia/cuvs-lucene/issues/93
  *   cuvsCagraSearch returned 0 (Reason=cudaErrorInvalidValue:invalid argument)
  *
  * ROOT CAUSE:

cpp/tests/neighbors/ann_cagra/bug_iterative_cagra_build.cu

@@ -1,85 +1,85 @@
-/*
- * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
- * SPDX-License-Identifier: Apache-2.0
- */
-
-#include <gtest/gtest.h>
-
-#include <cuvs/neighbors/cagra.hpp>
-
-#include <raft/core/device_mdarray.hpp>
-#include <raft/core/device_resources.hpp>
-#include <raft/random/rng.cuh>
-
-#include <cstdint>
-#include <type_traits>
-
-namespace cuvs::neighbors::cagra {
-
-template <typename DataT>
-class CagraIterativeBuildBugTest : public ::testing::Test {
- public:
-  using data_type = DataT;
-
- protected:
-  void run()
-  {
-    // Set up iterative CAGRA graph building
-    cagra::index_params index_params;
-    // The bug manifests when graph_degree is equal to intermediate_graph_degree
-    // see issue https://github.com/rapidsai/cuvs/issues/1818
-    index_params.graph_degree              = 16;
-    index_params.intermediate_graph_degree = 16;
-
-    // Use iterative CAGRA search for graph building
-    index_params.graph_build_params = graph_build_params::iterative_search_params();
-
-    // Build the index
-    auto cagra_index = cagra::build(res, index_params, raft::make_const_mdspan(dataset->view()));
-    raft::resource::sync_stream(res);
-
-    // Verify the index was built successfully
-    ASSERT_GT(cagra_index.size(), 0);
-    ASSERT_EQ(cagra_index.dim(), n_dim);
-  }
-
-  void SetUp() override
-  {
-    dataset.emplace(raft::make_device_matrix<data_type, int64_t>(res, n_samples, n_dim));
-    raft::random::RngState r(1234ULL);
-
-    // Generate random data based on type
-    if constexpr (std::is_same_v<data_type, float>) {
-      raft::random::normal(
-        res, r, dataset->data_handle(), n_samples * n_dim, data_type(0), data_type(1));
-    } else if constexpr (std::is_same_v<data_type, int8_t>) {
-      raft::random::uniformInt(
-        res, r, dataset->data_handle(), n_samples * n_dim, int8_t(-128), int8_t(127));
-    } else if constexpr (std::is_same_v<data_type, uint8_t>) {
-      raft::random::uniformInt(
-        res, r, dataset->data_handle(), n_samples * n_dim, uint8_t(0), uint8_t(255));
-    }
-    raft::resource::sync_stream(res);
-  }
-
-  void TearDown() override
-  {
-    dataset.reset();
-    raft::resource::sync_stream(res);
-  }
-
- private:
-  raft::resources res;
-  std::optional<raft::device_matrix<data_type, int64_t>> dataset = std::nullopt;
-
-  constexpr static int64_t n_samples = 10000;
-  constexpr static int64_t n_dim     = 1024;
-};
-
-// Instantiate test for different data types
-using TestTypes = ::testing::Types<float, int8_t, uint8_t>;
-TYPED_TEST_SUITE(CagraIterativeBuildBugTest, TestTypes);
-
-TYPED_TEST(CagraIterativeBuildBugTest, IterativeBuildTest) { this->run(); }
-
-}  // namespace cuvs::neighbors::cagra
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <gtest/gtest.h>
+
+#include <cuvs/neighbors/cagra.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/random/rng.cuh>
+
+#include <cstdint>
+#include <type_traits>
+
+namespace cuvs::neighbors::cagra {
+
+template <typename DataT>
+class CagraIterativeBuildBugTest : public ::testing::Test {
+ public:
+  using data_type = DataT;
+
+ protected:
+  void run()
+  {
+    // Set up iterative CAGRA graph building
+    cagra::index_params index_params;
+    // The bug manifests when graph_degree is equal to intermediate_graph_degree
+    // see issue https://github.com/nvidia/cuvs/issues/1818
+    index_params.graph_degree              = 16;
+    index_params.intermediate_graph_degree = 16;
+
+    // Use iterative CAGRA search for graph building
+    index_params.graph_build_params = graph_build_params::iterative_search_params();
+
+    // Build the index
+    auto cagra_index = cagra::build(res, index_params, raft::make_const_mdspan(dataset->view()));
+    raft::resource::sync_stream(res);
+
+    // Verify the index was built successfully
+    ASSERT_GT(cagra_index.size(), 0);
+    ASSERT_EQ(cagra_index.dim(), n_dim);
+  }
+
+  void SetUp() override
+  {
+    dataset.emplace(raft::make_device_matrix<data_type, int64_t>(res, n_samples, n_dim));
+    raft::random::RngState r(1234ULL);
+
+    // Generate random data based on type
+    if constexpr (std::is_same_v<data_type, float>) {
+      raft::random::normal(
+        res, r, dataset->data_handle(), n_samples * n_dim, data_type(0), data_type(1));
+    } else if constexpr (std::is_same_v<data_type, int8_t>) {
+      raft::random::uniformInt(
+        res, r, dataset->data_handle(), n_samples * n_dim, int8_t(-128), int8_t(127));
+    } else if constexpr (std::is_same_v<data_type, uint8_t>) {
+      raft::random::uniformInt(
+        res, r, dataset->data_handle(), n_samples * n_dim, uint8_t(0), uint8_t(255));
+    }
+    raft::resource::sync_stream(res);
+  }
+
+  void TearDown() override
+  {
+    dataset.reset();
+    raft::resource::sync_stream(res);
+  }
+
+ private:
+  raft::resources res;
+  std::optional<raft::device_matrix<data_type, int64_t>> dataset = std::nullopt;
+
+  constexpr static int64_t n_samples = 10000;
+  constexpr static int64_t n_dim     = 1024;
+};
+
+// Instantiate test for different data types
+using TestTypes = ::testing::Types<float, int8_t, uint8_t>;
+TYPED_TEST_SUITE(CagraIterativeBuildBugTest, TestTypes);
+
+TYPED_TEST(CagraIterativeBuildBugTest, IterativeBuildTest) { this->run(); }
+
+}  // namespace cuvs::neighbors::cagra

cpp/tests/neighbors/ann_ivf_flat.cuh

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION.
  * SPDX-License-Identifier: Apache-2.0
  */
 #pragma once
@@ -545,7 +545,7 @@ const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
   {1000, 10000, 2050, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},
   {1000, 10000, 2050, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   // TODO: Re-enable test after adjusting parameters for higher recall. See
-  // https://github.com/rapidsai/cuvs/issues/1091
+  // https://github.com/nvidia/cuvs/issues/1091
   // {1000, 10000, 2051, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
   {1000, 10000, 2051, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 10000, 2052, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},