[skip ci]update ann-benchmarks result in README.md (#75)

Signed-off-by: Xiangyu Wang <wxy407827@antgroup.com>

README.md

@@ -12,7 +12,7 @@ ____    ____   _______.     ___       _______
 VSAG is a vector indexing library used for similarity search. The indexing algorithm allows users to search through various sizes of vector sets, especially those that cannot fit in memory. The library also provides methods for generating parameters based on vector dimensions and data scale, allowing developers to use it without understanding the algorithm’s principles. VSAG is written in C++ and provides a Python wrapper package called pyvsag. Developed by the Vector Database Team at Ant Group.
 
 ## Performance
-VSAG provides an optimized HNSW implementation that achieves state-of-the-art (SOTA) performance on the GIST dataset.
+The VSAG algorithm achieves a significant boost of efficiency and outperforms the previous **state-of-the-art (SOTA)** by a clear margin. Specifically, VSAG's QPS exceeds that of the previous SOTA algorithm, Glass, by over 100%, and the baseline algorithm, HNSWLIB, by over 300% according to the ann-benchmark result on the GIST dataset at 90% recall.
 The test in [ann-benchmarks](https://ann-benchmarks.com/) is running on an r6i.16xlarge machine on AWS with `--parallelism 31`, single-CPU, and hyperthreading disabled. 
 The result is as follows: