Dynamic IVF Index Implementation

bindings/python/CMakeLists.txt

@@ -43,6 +43,7 @@ set(CPP_FILES
 # ivf
 if (SVS_EXPERIMENTAL_ENABLE_IVF)
     list(APPEND CPP_FILES
+        src/dynamic_ivf.cpp
         src/ivf.cpp
     )
 endif()

bindings/python/include/svs/python/dynamic_ivf.h

@@ -0,0 +1,39 @@
+/*
+ * Copyright 2025 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+// svs python bindings
+#include "svs/python/core.h"
+
+#include <pybind11/pybind11.h>
+
+namespace svs::python::dynamic_ivf {
+
+// Specializations
+template <typename F> void for_standard_specializations(F&& f) {
+#define X(Q, T, N) f.template operator()<Q, T, N>()
+    X(float, float, Dynamic);
+    X(float, float, Dynamic);
+    X(float, svs::Float16, Dynamic);
+    X(float, svs::Float16, Dynamic);
+    X(float, svs::BFloat16, Dynamic);
+    X(float, svs::BFloat16, Dynamic);
+#undef X
+}
+
+void wrap(pybind11::module& m);
+} // namespace svs::python::dynamic_ivf

bindings/python/include/svs/python/ivf.h

@@ -20,7 +20,9 @@
 #include "svs/python/common.h"
 #include "svs/python/core.h"
 
+#include "svs/core/data/simple.h"
 #include "svs/core/distance.h"
+#include "svs/index/ivf/clustering.h"
 #include "svs/lib/bfloat16.h"
 #include "svs/lib/datatype.h"
 #include "svs/lib/float16.h"
@@ -30,6 +32,8 @@
 #include <pybind11/pybind11.h>
 #include <pybind11/stl/filesystem.h>
 
+#include <variant>
+
 namespace svs::python {
 namespace ivf_specializations {
 ///
@@ -61,6 +65,18 @@ template <typename F> void for_standard_specializations(F&& f) {
 } // namespace ivf_specializations
 
 namespace ivf {
+
+// The build process in IVF uses Kmeans to get centroids and assignments of data.
+// This sparse clustering can be saved with centroids stored as float datatype.
+// While assembling, the sparse clustering is used to create DenseClusters and
+// centroids datatype can be changed as per the search specializations.
+// Support both BFloat16 and Float16 centroids to match data types and leverage AMX.
+using ClusteringBF16 =
+    svs::index::ivf::Clustering<svs::data::SimpleData<svs::BFloat16>, uint32_t>;
+using ClusteringF16 =
+    svs::index::ivf::Clustering<svs::data::SimpleData<svs::Float16>, uint32_t>;
+using Clustering = std::variant<ClusteringBF16, ClusteringF16>;
+
 template <typename Manager> void add_interface(pybind11::class_<Manager>& manager) {
     manager.def_property_readonly(
         "experimental_backend_string",
@@ -82,6 +98,28 @@ template <typename Manager> void add_interface(pybind11::class_<Manager>& manage
 
             See also: `svs.IVFSearchParameters`.)"
     );
+
+    manager.def(
+        "get_distance",
+        [](const Manager& index, size_t id, const py_contiguous_array_t<float>& query) {
+            return index.get_distance(id, as_span(query));
+        },
+        pybind11::arg("id"),
+        pybind11::arg("query"),
+        R"(
+        Compute the distance between a query vector and a vector in the index.
+
+        Args:
+            id: The ID of the vector in the index.
+            query: The query vector as a numpy array.
+
+        Returns:
+            The distance between the query and the indexed vector.
+
+        Raises:
+            RuntimeError: If the ID doesn't exist or dimensions don't match.
+        )"
+    );
 }
 
 void wrap(pybind11::module& m);