Add DiskANN index type

Milvus.Client.Tests/IndexTests.cs

@@ -47,6 +47,7 @@ public async Task Create_scalar_index()
     [InlineData(IndexType.IvfSq8, """{ "nlist": "8" }""")]
     [InlineData(IndexType.IvfPq, """{ "nlist": "8", "m": "4" }""")]
     [InlineData(IndexType.Hnsw, """{ "efConstruction": "8", "M": "4" }""")]
+    [InlineData(IndexType.DiskANN, """{ "nlist": "8" }""")]
     [InlineData(IndexType.AutoIndex, """{ }""")]
     public async Task Index_types_float(IndexType indexType, string extraParamsString)
     {

Milvus.Client/IndexType.cs

@@ -97,6 +97,13 @@ public enum IndexType
     /// </remarks>
     Hnsw,
 
+    /// <summary>
+    /// SCANN (Score-aware quantization loss) is similar to <see cref="IvfPq" /> in terms of vector clustering and
+    /// product quantization. What makes them different lies in the implementation details of product quantization and
+    /// the use of SIMD (Single-Instruction / Multi-data) for efficient calculation.
+    /// </summary>
+    DiskANN,
+
     /// <summary>
     /// ANNOY (Approximate Nearest Neighbors Oh Yeah) is an index that uses a hyperplane to divide a high-dimensional
     /// space into multiple subspaces, and then stores them in a tree structure.

Milvus.Client/MilvusCollection.Index.cs

@@ -75,6 +75,7 @@ static string GetGrpcIndexType(IndexType indexType)
                 IndexType.IvfPq => "IVF_PQ",
                 IndexType.IvfSq8 => "IVF_SQ8",
                 IndexType.Hnsw => "HNSW",
+                IndexType.DiskANN => "DISKANN",
                 IndexType.RhnswFlat => "RHNSW_FLAT",
                 IndexType.RhnswPq => "RHNSW_PQ",
                 IndexType.RhnswSq => "RHNSW_SQ",