Fixing docs accordingly

docs/source/conf.py

@@ -71,7 +71,7 @@
 
 .. attention::
 
-  The vector search and clustering algorithms in RAFT are being migrated to a new library dedicated to vector search called `cuVS <https://github.com/rapidsai/cuvs>`_. We will continue to support the vector search algorithms in RAFT during this move, but will no longer update them after the RAPIDS 24.06 (June) release. We plan to complete the migration by RAPIDS 24.08 (August) release.
+  The vector search and clustering algorithms in RAFT are being migrated to a new library dedicated to vector search called `cuVS <https://github.com/rapidsai/cuvs>`_. We will continue to support the vector search algorithms in RAFT during this move, but will no longer update them after the RAPIDS 24.06 (June) release. We plan to complete the migration by RAPIDS 24.10 (October) release and they will be removed from RAFT altogether in the 24.12 (December) release.
 
 """
 

docs/source/raft_ann_benchmarks.md

@@ -2,6 +2,10 @@
 
 This project provides a benchmark program for various ANN search implementations. It's especially suitable for comparing GPU implementations as well as comparing GPU against CPU.
 
+> [!IMPORTANT]
+> The vector search and clustering algorithms in RAFT are being migrated to a new library dedicated to vector search called [cuVS](https://github.com/rapidsai/cuvs). As a result, `raft-ann-bench` is being migrated to `cuvs-bench` and will be removed from RAFT altogether in the 24.12 (December) release.
+
+
 ## Table of Contents
 
 - [Installing the benchmarks](#installing-the-benchmarks)
@@ -279,15 +283,15 @@ python -m raft_ann_bench.plot --dataset deep-image-96-inner
 
 Configuration files already exist for the following list of the million-scale datasets. Please refer to [ann-benchmarks datasets](https://github.com/erikbern/ann-benchmarks/#data-sets) for more information, including actual train and sizes. These all work out-of-the-box with the `--dataset` argument. Other million-scale datasets from `ann-benchmarks.com` will work, but will require a json configuration file to be created in `$CONDA_PREFIX/lib/python3.xx/site-packages/raft_ann_bench/run/conf`, or you can specify the `--configuration` option to use a specific file.
 
-| Dataset Name | Train Rows | Columns | Test Rows      | Distance   | 
+**| Dataset Name | Train Rows | Columns | Test Rows      | Distance   | 
 |-----|------------|----|----------------|------------|
 | `deep-image-96-angular` | 10M        | 96 | 10K            | Angular    |
 | `fashion-mnist-784-euclidean` | 60K        | 784 | 10K |  Euclidean |
 | `glove-50-angular` | 1.1M       | 50 | 10K | Angular |
 | `glove-100-angular` | 1.1M | 100 | 10K | Angular |
 | `mnist-784-euclidean` | 60K | 784 | 10K | Euclidean |
 | `nytimes-256-angular` | 290K | 256 | 10K | Angular |
-| `sift-128-euclidean` | 1M | 128 | 10K | Euclidean|
+| `sift-128-euclidean` | 1M | 128 | 10K | Euclidean|**
 
 All of the datasets above contain ground test datasets with 100 neighbors. Thus `k` for these datasets must be  less than or equal to 100.