fix: update diskann config for config check

include/knowhere/config.h

@@ -508,6 +508,8 @@ class BaseConfig : public Config {
     CFG_BOOL retrieve_friendly;
     CFG_STRING data_path;
     CFG_STRING index_prefix;
+    // the size of the raw vector data
+    CFG_FLOAT vec_field_size_gb;
     // for distance metrics, we search for vectors with distance in [range_filter, radius).
     // for similarity metrics, we search for vectors with similarity in (radius, range_filter].
     CFG_FLOAT radius;
@@ -559,6 +561,10 @@ class BaseConfig : public Config {
             .allow_empty_without_default()
             .for_train()
             .for_deserialize();
+        KNOWHERE_CONFIG_DECLARE_FIELD(vec_field_size_gb)
+            .description("the size (in GB) of the raw vector data.")
+            .set_default(0)
+            .for_train();
         KNOWHERE_CONFIG_DECLARE_FIELD(k)
             .set_default(10)
             .description("search for top k similar vector.")

src/index/diskann/diskann_config.h

@@ -33,6 +33,11 @@ class DiskANNConfig : public BaseConfig {
     // complexity. Plz set this value larger than the max_degree unless you need to build indices really quickly and can
     // somewhat compromise on quality.
     CFG_INT search_list_size;
+
+    // The ratio of the size reserved for the pq code to the size of the raw data (defined with vec_field_size_gb)
+    // This parameter will replace pq_code_budget_gb to avoid calculating the actual size on the Milvus side.
+    // The index can indirectly obtain pq_code_budget_gb by vec_field_size_gb * pq_code_budget_gb_ratio
+    CFG_FLOAT pq_code_budget_gb_ratio;
     // Limit the size of the PQ code after the raw vector has been PQ-encoded. PQ code is a (pq_code_budget_gb * 1024 *
     // 1024 * 1024) / row_num)-dimensional uint8 vector. If pq_code_budget_gb is too large, it will be adjusted to the
     // size of dim*row_num.
@@ -50,6 +55,12 @@ class DiskANNConfig : public BaseConfig {
     // This is the flag to enable fast build, in which we will not build vamana graph by full 2 round. This can
     // accelerate index build ~30% with an ~1% recall regression.
     CFG_BOOL accelerate_build;
+
+    // The ratio of the size reserved for the search cache to the size of the raw data (defined with vec_field_size_gb)
+    // This parameter will replace pq_code_budget_gb to avoid calculating the actual size on the Milvus side.
+    // The index can indirectly obtain search_cache_budget_gb by vec_field_size_gb * search_cache_budget_gb_ratio
+    CFG_FLOAT search_cache_budget_gb_ratio;
+
     // While serving the index, the entire graph is stored on SSD. For faster search performance, you can cache a few
     // frequently accessed nodes in memory.
     CFG_FLOAT search_cache_budget_gb;
@@ -86,12 +97,19 @@ class DiskANNConfig : public BaseConfig {
             .for_search()
             .for_range_search()
             .for_iterator();
+        KNOWHERE_CONFIG_DECLARE_FIELD(pq_code_budget_gb_ratio)
+            .description("the size of PQ compared with vector field data")
+            .set_default(0)
+            .set_range(0, std::numeric_limits<CFG_FLOAT::value_type>::max())
+            .for_train();
         KNOWHERE_CONFIG_DECLARE_FIELD(pq_code_budget_gb)
-            .description("the size of PQ compressed representation in GB.")
+            .description("the ratio of the size reserved for the pq code to the size of the raw data.")
+            .set_default(0)
             .set_range(0, std::numeric_limits<CFG_FLOAT::value_type>::max())
             .for_train();
         KNOWHERE_CONFIG_DECLARE_FIELD(build_dram_budget_gb)
             .description("limit on the memory allowed for building the index in GB.")
+            .set_default(0)
             .set_range(0, std::numeric_limits<CFG_FLOAT::value_type>::max())
             .for_train();
         KNOWHERE_CONFIG_DECLARE_FIELD(disk_pq_dims)
@@ -102,6 +120,12 @@ class DiskANNConfig : public BaseConfig {
             .description("a flag to enbale fast build.")
             .set_default(false)
             .for_train();
+        KNOWHERE_CONFIG_DECLARE_FIELD(search_cache_budget_gb_ratio)
+            .description("the ratio of the size reserved for the search cache to the size of the raw data.")
+            .set_default(0)
+            .set_range(0, std::numeric_limits<CFG_FLOAT::value_type>::max())
+            .for_train()
+            .for_deserialize();
         KNOWHERE_CONFIG_DECLARE_FIELD(search_cache_budget_gb)
             .description("the size of cached nodes in GB.")
             .set_default(0)
@@ -148,6 +172,10 @@ class DiskANNConfig : public BaseConfig {
                 if (!search_list_size.has_value()) {
                     search_list_size = kDefaultSearchListSizeForBuild;
                 }
+                pq_code_budget_gb =
+                    std::max(pq_code_budget_gb.value(), pq_code_budget_gb_ratio.value() * vec_field_size_gb.value());
+                search_cache_budget_gb = std::max(search_cache_budget_gb.value(),
+                                                  search_cache_budget_gb_ratio.value() * vec_field_size_gb.value());
                 break;
             }
             case PARAM_TYPE::SEARCH: {

tests/ut/test_diskann.cc

@@ -64,6 +64,52 @@ WriteRawDataToDisk(const std::string data_path, const DataType* raw_data, const
 }
 
 }  // namespace
+TEST_CASE("Valid diskann build params test", "[diskann]") {
+    int rows_num = 1000000;
+    auto version = GenTestVersionList();
+
+    auto ratio = GENERATE(as<float>{}, 0.01, 0.1, 0.125);
+
+    float pq_code_budget_gb = sizeof(float) * kDim * rows_num * 0.125 / (1024 * 1024 * 1024);
+    float search_cache_budget_gb = sizeof(float) * kDim * rows_num * 0.05 / (1024 * 1024 * 1024);
+
+    auto test_gen = [&]() {
+        knowhere::Json json;
+        json["dim"] = kDim;
+        json["metric_type"] = "L2";
+        json["k"] = 100;
+        json["index_prefix"] = kL2IndexPrefix;
+        json["data_path"] = kRawDataPath;
+        json["max_degree"] = 24;
+        json["search_list_size"] = 64;
+        json["vec_field_size_gb"] = 1.0;
+        json["pq_code_budget_gb_ratio"] = ratio;
+        json["pq_code_budget_gb"] = pq_code_budget_gb;
+        json["build_dram_budget_gb"] = 32.0;
+        json["search_cache_budget_gb_ratio"] = ratio;
+        json["search_cache_budget_gb"] = search_cache_budget_gb;
+        json["beamwidth"] = 8;
+        json["min_k"] = 10;
+        json["max_k"] = 8000;
+        return json;
+    };
+
+    SECTION("Dynamic param check") {
+        knowhere::Json test_json = test_gen();
+
+        auto cfg = knowhere::IndexStaticFaced<float>::CreateConfig(knowhere::IndexEnum::INDEX_DISKANN, version);
+        knowhere::Json json_(test_json);
+        std::string msg;
+        auto res = knowhere::Config::FormatAndCheck(*cfg, json_, &msg);
+        REQUIRE(res == knowhere::Status::success);
+        res = knowhere::Config::Load(*cfg, json_, knowhere::PARAM_TYPE::TRAIN, &msg);
+        REQUIRE(res == knowhere::Status::success);
+
+        knowhere::DiskANNConfig diskCfg = static_cast<const knowhere::DiskANNConfig&>(*cfg);
+        REQUIRE(diskCfg.pq_code_budget_gb == std::max(pq_code_budget_gb, 1.0f * ratio));
+        REQUIRE(diskCfg.search_cache_budget_gb == std::max(search_cache_budget_gb, 1.0f * ratio));
+    }
+}
 
 TEST_CASE("Invalid diskann params test", "[diskann]") {
     fs::remove_all(kDir);