[SVS] Add TieredSVSIndex flow tests.

src/python_bindings/bindings.cpp

@@ -594,10 +594,15 @@ class PyTiered_SVSIndex : public PyTieredIndex {
     explicit PyTiered_SVSIndex(const SVSParams &svs_params,
                                const TieredSVSParams &tiered_svs_params, size_t buffer_limit) {
 
-        // Create primaryIndexParams and specific params for hnsw tiered index.
+        // Create primaryIndexParams and specific params for svs tiered index.
         VecSimParams primary_index_params = {.algo = VecSimAlgo_SVS,
                                              .algoParams = {.svsParams = svs_params}};
 
+        if (primary_index_params.algoParams.svsParams.num_threads == 0) {
+            primary_index_params.algoParams.svsParams.num_threads =
+                this->mock_thread_pool.thread_pool_size; // Use the mock thread pool size as default
+        }
+
         auto tiered_params = this->getTieredIndexParams(buffer_limit);
         tiered_params.primaryIndexParams = &primary_index_params;
         tiered_params.specificParams.tieredSVSParams = tiered_svs_params;
@@ -611,6 +616,10 @@ class PyTiered_SVSIndex : public PyTieredIndex {
         // Set the created tiered index in the index external context.
         this->mock_thread_pool.ctx->index_strong_ref = this->index;
     }
+
+    size_t SVSLabelCount() {
+        return this->index->debugInfo().tieredInfo.backendCommonInfo.indexLabelCount;
+    }
 };
 #endif
 
@@ -672,10 +681,13 @@ PYBIND11_MODULE(VecSim, m) {
 
     py::enum_<VecSimSvsQuantBits>(m, "VecSimSvsQuantBits")
         .value("VecSimSvsQuant_NONE", VecSimSvsQuant_NONE)
-        .value("VecSimSvsQuant_8", VecSimSvsQuant_8)
+        .value("VecSimSvsQuant_Scalar", VecSimSvsQuant_Scalar)
         .value("VecSimSvsQuant_4", VecSimSvsQuant_4)
+        .value("VecSimSvsQuant_8", VecSimSvsQuant_8)
         .value("VecSimSvsQuant_4x4", VecSimSvsQuant_4x4)
         .value("VecSimSvsQuant_4x8", VecSimSvsQuant_4x8)
+        .value("VecSimSvsQuant_4x8_LeanVec", VecSimSvsQuant_4x8_LeanVec)
+        .value("VecSimSvsQuant_8x8_LeanVec", VecSimSvsQuant_8x8_LeanVec)
         .export_values();
 
     py::class_<SVSParams>(m, "SVSParams")
@@ -705,6 +717,7 @@ PYBIND11_MODULE(VecSim, m) {
     py::class_<TieredSVSParams>(m, "TieredSVSParams")
         .def(py::init())
         .def_readwrite("trainingTriggerThreshold", &TieredSVSParams::trainingTriggerThreshold)
+        .def_readwrite("updateTriggerThreshold", &TieredSVSParams::updateTriggerThreshold)
         .def_readwrite("updateJobWaitTime", &TieredSVSParams::updateJobWaitTime);
 
     py::class_<AlgoParams>(m, "AlgoParams")
@@ -799,7 +812,9 @@ PYBIND11_MODULE(VecSim, m) {
                          size_t flat_buffer_size = DEFAULT_BLOCK_SIZE) {
                  return new PyTiered_SVSIndex(svs_params, tiered_svs_params, flat_buffer_size);
              }),
-             py::arg("svs_params"), py::arg("tiered_svs_params"), py::arg("flat_buffer_size"));
+             py::arg("svs_params"), py::arg("tiered_svs_params"),
+             py::arg("flat_buffer_size") = DEFAULT_BLOCK_SIZE)
+        .def("svs_label_count", &PyTiered_SVSIndex::SVSLabelCount);
 #endif
 
     py::class_<PyBatchIterator>(m, "BatchIterator")

tests/flow/common.py

@@ -13,6 +13,28 @@
 import math
 from ml_dtypes import bfloat16
 
+# alpha = 0 means the default value, which is 1.2 for L2 and 0.9 for other metrics.
+def create_svs_params (dim, num_elements, data_type, metric, quantBits = VecSimSvsQuant_NONE, alpha = 0,
+                       graph_max_degree = 32, construction_window_size = 200, search_window_size = 10,
+                       max_candidate_pool_size = 0, prune_to = 0, epsilon = 0.01, num_threads = 0):
+    svs_params = SVSParams()
+
+    svs_params.dim = dim
+    svs_params.type = data_type
+    svs_params.metric = metric
+    svs_params.quantBits = quantBits
+    svs_params.alpha = alpha
+    svs_params.graph_max_degree = graph_max_degree
+    svs_params.construction_window_size = construction_window_size
+    svs_params.max_candidate_pool_size = max_candidate_pool_size
+    svs_params.prune_to = prune_to
+    svs_params.use_search_history = VecSimOption_AUTO # VecSimOption_AUTO means use the default value
+    svs_params.search_window_size = search_window_size
+    svs_params.epsilon = epsilon
+    svs_params.num_threads = num_threads
+
+    return svs_params
+
 def create_hnsw_params(dim, num_elements, metric, data_type, ef_construction=200, m=16, ef_runtime=10, epsilon=0.01,
                       is_multi=False):
     hnsw_params = HNSWParams()