In this talk, we delve into the groundbreaking vector search framework, PyANNs, highlighting its four key features and main contributions to the field. Firstly, PyANNs unifies the search process for diverse vector types, seamlessly handling both dense and sparse vectors, float and integer vectors. Secondly, its adaptability extends to different graph types, unifying the search process for multi-layered graphs like HNSW and single-layered graphs like Vamana. Thirdly, PyANNs achieves unprecedented computation speeds through the utilization of various vector instruction sets, establishing itself as the pinnacle of performance. Lastly, the framework incorporates an adaptive computation and memory optimization process, ensuring maximum vector computation bandwidth across diverse machines.