feat(rust): add search_with_filter to CAGRA Index

Add `Index::search_with_filter()` that accepts a bitset filter via
DLPack ManagedTensor. The C API `cuvsCagraSearch()` already supports
`cuvsFilter` with BITSET type, but the Rust bindings hardcoded
`NO_FILTER`. This exposes the existing capability.

The bitset is a 1-D uint32 device tensor with ceil(n_rows / 32)
elements. Bit = 1 includes the row, bit = 0 excludes it. Filtering
happens during graph traversal, not post-retrieval.

Includes test: builds a 256-point index, filters to even-indexed
rows, verifies all returned neighbors pass the filter.

Author: jamie8johnson

rust/cuvs/src/cagra/index.rs

@@ -97,6 +97,48 @@ impl Index {
         }
     }
 
+    /// Perform a filtered Approximate Nearest Neighbors search on the Index
+    ///
+    /// Like [`search`](Self::search), but accepts a bitset filter to exclude
+    /// vectors during graph traversal. Filtered vectors are never visited,
+    /// giving better recall than post-filtering.
+    ///
+    /// # Arguments
+    ///
+    /// * `res` - Resources to use
+    /// * `params` - Parameters to use in searching the index
+    /// * `queries` - A matrix in device memory to query for
+    /// * `neighbors` - Matrix in device memory that receives the indices of the nearest neighbors
+    /// * `distances` - Matrix in device memory that receives the distances of the nearest neighbors
+    /// * `bitset` - A 1-D `uint32` device tensor with `ceil(n_rows / 32)` elements.
+    ///   Each bit corresponds to a dataset row: bit 1 = include, bit 0 = exclude.
+    pub fn search_with_filter(
+        &self,
+        res: &Resources,
+        params: &SearchParams,
+        queries: &ManagedTensor,
+        neighbors: &ManagedTensor,
+        distances: &ManagedTensor,
+        bitset: &ManagedTensor,
+    ) -> Result<()> {
+        unsafe {
+            let prefilter = ffi::cuvsFilter {
+                addr: bitset.as_ptr() as usize,
+                type_: ffi::cuvsFilterType::BITSET,
+            };
+
+            check_cuvs(ffi::cuvsCagraSearch(
+                res.0,
+                params.0,
+                self.0,
+                queries.as_ptr(),
+                neighbors.as_ptr(),
+                distances.as_ptr(),
+                prefilter,
+            ))
+        }
+    }
+
     /// Save the CAGRA index to file.
     ///
     /// Experimental, both the API and the serialization format are subject to change.
@@ -254,6 +296,76 @@ mod tests {
         test_cagra(build_params);
     }
 
+    /// Test bitset-filtered search: exclude odd-indexed rows, verify they don't appear.
+    #[test]
+    fn test_cagra_search_with_filter() {
+        let res = Resources::new().unwrap();
+        let build_params = IndexParams::new().unwrap();
+
+        let n_datapoints = 256;
+        let n_features = 16;
+        let dataset =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+        let index =
+            Index::build(&res, &build_params, &dataset).expect("failed to create cagra index");
+
+        // Build a bitset that includes only even-indexed rows
+        let n_words = (n_datapoints + 31) / 32;
+        let mut bitset_host = ndarray::Array::<u32, _>::zeros(ndarray::Ix1(n_words));
+        for i in 0..n_datapoints {
+            if i % 2 == 0 {
+                bitset_host[i / 32] |= 1u32 << (i % 32);
+            }
+        }
+        let bitset = ManagedTensor::from(&bitset_host).to_device(&res).unwrap();
+
+        // Query with the first 4 even-indexed rows
+        let n_queries = 4;
+        let queries = dataset.slice(s![0..n_queries * 2;2, ..]); // rows 0, 2, 4, 6
+        let queries = ManagedTensor::from(&queries).to_device(&res).unwrap();
+
+        let k = 10;
+        let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
+        let neighbors = ManagedTensor::from(&neighbors_host)
+            .to_device(&res)
+            .unwrap();
+        let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+        let distances = ManagedTensor::from(&distances_host)
+            .to_device(&res)
+            .unwrap();
+
+        let search_params = SearchParams::new().unwrap();
+
+        index
+            .search_with_filter(
+                &res,
+                &search_params,
+                &queries,
+                &neighbors,
+                &distances,
+                &bitset,
+            )
+            .unwrap();
+
+        neighbors.to_host(&res, &mut neighbors_host).unwrap();
+
+        // All returned neighbors must be even-indexed (odd rows are filtered out).
+        for q in 0..n_queries {
+            for n in 0..k {
+                let neighbor_id = neighbors_host[[q, n]];
+                assert_eq!(
+                    neighbor_id % 2,
+                    0,
+                    "query {q}, neighbor {n}: got odd index {neighbor_id}, expected only even"
+                );
+            }
+        }
+
+        // First query (row 0) should find itself as the nearest neighbor.
+        assert_eq!(neighbors_host[[0, 0]], 0);
+    }
+
     /// Test that an index can be searched multiple times without rebuilding.
     /// This validates that `search()` takes `&self` instead of `self`.
     #[test]